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Exam Code: Google-ACE Practice exam 2022 by team
Google-ACE Google Associate Cloud Engineer - 2022

Length: Two hours
Languages: English, Japanese, Spanish, Indonesian.
Exam format: Multiple choice and multiple select, taken in person at a test center. Locate a Exam Center near you.
Prerequisites: None
Recommended experience: 6 months+ hands-on experience with Google Cloud

An Associate Cloud Engineer deploys applications, monitors operations, and manages enterprise solutions. This individual is able to use Google Cloud Console and the command-line interface to perform common platform-based tasks to maintain one or more deployed solutions that leverage Google-managed or self-managed services on Google Cloud.

The Associate Cloud Engineer exam assesses your ability to:

Set up a cloud solution environment
Plan and configure a cloud solution
Deploy and implement a cloud solution
Ensure successful operation of a cloud solution
Configure access and security

Setting up a cloud solution environment
Setting up cloud projects and accounts. Activities include:

Creating projects
Assigning users to predefined IAM roles within a project
Managing users in Cloud Identity (manually and automated)
Enabling APIs within projects
Provisioning one or more Stackdriver workspaces

Managing billing configuration. Activities include:

Creating one or more billing accounts
Linking projects to a billing account
Establishing billing budgets and alerts
Setting up billing exports to estimate daily/monthly charges

Installing and configuring the command line interface (CLI), specifically the Cloud SDK (e.g., setting the default project).

Planning and configuring a cloud solution
Planning and estimating GCP product use using the Pricing Calculator
Planning and configuring compute resources. Considerations include:

Selecting appropriate compute choices for a given workload (e.g., Compute Engine, Google Kubernetes Engine, App Engine, Cloud Run, Cloud Functions)
Using preemptible VMs and custom machine types as appropriate

Planning and configuring data storage options. Considerations include:

Product choice (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Bigtable)
Choosing storage options (e.g., Standard, Nearline, Coldline, Archive)

Planning and configuring network resources. Tasks include:

Differentiating load balancing options
Identifying resource locations in a network for availability
Configuring Cloud DNS
Deploying and implementing a cloud solution
Deploying and implementing Compute Engine resources. Tasks include:

Launching a compute instance using Cloud Console and Cloud SDK (gcloud) (e.g., assign disks, availability policy, SSH keys)
Creating an autoscaled managed instance group using an instance template
Generating/uploading a custom SSH key for instances
Configuring a VM for Stackdriver monitoring and logging
Assessing compute quotas and requesting increases
Installing the Stackdriver Agent for monitoring and logging

Deploying and implementing Google Kubernetes Engine resources. Tasks include:

Deploying a Google Kubernetes Engine cluster
Deploying a container application to Google Kubernetes Engine using pods
Configuring Google Kubernetes Engine application monitoring and logging
Deploying and implementing App Engine, Cloud Run, and Cloud Functions resources. Tasks include, where applicable:

Deploying an application, updating scaling configuration, versions, and traffic splitting
Deploying an application that receives Google Cloud events (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)

Deploying and implementing data solutions. Tasks include:

Initializing data systems with products (e.g., Cloud SQL, Cloud Datastore, BigQuery, Cloud Spanner, Cloud Pub/Sub, Cloud Bigtable, Cloud Dataproc, Cloud Dataflow, Cloud Storage)
Loading data (e.g., command line upload, API transfer, import/export, load data from Cloud Storage, streaming data to Cloud Pub/Sub)

Deploying and implementing networking resources. Tasks include:

Creating a VPC with subnets (e.g., custom-mode VPC, shared VPC)
Launching a Compute Engine instance with custom network configuration (e.g., internal-only IP address, Google private access, static external and private IP address, network tags)
Creating ingress and egress firewall rules for a VPC (e.g., IP subnets, tags, service accounts)
Creating a VPN between a Google VPC and an external network using Cloud VPN
Creating a load balancer to distribute application network traffic to an application (e.g., Global HTTP(S) load balancer, Global SSL Proxy load balancer, Global TCP Proxy load balancer, regional network load balancer, regional internal load balancer)

Deploying a solution using Cloud Marketplace. Tasks include:

Browsing Cloud Marketplace catalog and viewing solution details
Deploying a Cloud Marketplace solution
Deploying application infrastructure using Cloud Deployment Manager. Tasks include:

Developing Deployment Manager templates
Launching a Deployment Manager template

Ensuring successful operation of a cloud solution
Managing Compute Engine resources. Tasks include:

Managing a single VM instance (e.g., start, stop, edit configuration, or delete an instance)
SSH/RDP to the instance
Attaching a GPU to a new instance and installing CUDA libraries
Viewing current running VM inventory (instance IDs, details)
Working with snapshots (e.g., create a snapshot from a VM, view snapshots, delete a snapshot)
Working with images (e.g., create an image from a VM or a snapshot, view images, delete an image)
Working with instance groups (e.g., set autoscaling parameters, assign instance template, create an instance template, remove instance group)
Working with management interfaces (e.g., Cloud Console, Cloud Shell, GCloud SDK)

Managing Google Kubernetes Engine resources. Tasks include:

Viewing current running cluster inventory (nodes, pods, services)
Browsing the container image repository and viewing container image details
Working with node pools (e.g., add, edit, or remove a node pool)
Working with pods (e.g., add, edit, or remove pods)
Working with services (e.g., add, edit, or remove a service)
Working with stateful applications (e.g. persistent volumes, stateful sets)
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)

Managing App Engine and Cloud Run resources. Tasks include:

Adjusting application traffic splitting parameters
Setting scaling parameters for autoscaling instances
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)

Managing storage and database solutions. Tasks include:

Moving objects between Cloud Storage buckets
Converting Cloud Storage buckets between storage classes
Setting object life cycle management policies for Cloud Storage buckets
Executing queries to retrieve data from data instances (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Datastore, Cloud Bigtable)
Estimating costs of a BigQuery query
Backing up and restoring data instances (e.g., Cloud SQL, Cloud Datastore)
Reviewing job status in Cloud Dataproc, Cloud Dataflow, or BigQuery
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)

Managing networking resources. Tasks include:

Adding a subnet to an existing VPC
Expanding a subnet to have more IP addresses
Reserving static external or internal IP addresses
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)

Monitoring and logging. Tasks include:

Creating Stackdriver alerts based on resource metrics
Creating Stackdriver custom metrics
Configuring log sinks to export logs to external systems (e.g., on-premises or BigQuery)
Viewing and filtering logs in Stackdriver
Viewing specific log message details in Stackdriver
Using cloud diagnostics to research an application issue (e.g., viewing Cloud Trace data, using Cloud Debug to view an application point-in-time)
Viewing Google Cloud Platform status
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)

Configuring access and security
Managing identity and access management (IAM). Tasks include:

Viewing IAM role assignments
Assigning IAM roles to accounts or Google Groups
Defining custom IAM roles

Managing service accounts. Tasks include:

Managing service accounts with limited privileges
Assigning a service account to VM instances
Granting access to a service account in another project

Viewing audit logs for project and managed services.

Google Associate Cloud Engineer - 2022
Google Associate Study Guide
Killexams : Google Associate Study Guide - BingNews Search results Killexams : Google Associate Study Guide - BingNews Killexams : A guide to Google Analytics 4 for marketing agencies

On July 1, 2023, Google will move everyone to its latest version, Google Analytics 4 (GA4), and retire Google Analytics 3 (also known as Universal Analytics or UA). While these changes will benefit the average user without any noticeable difference in how they search and browse online, the switch will require significant changes for marketers and businesses.

Here’s everything you need to know about Google Analytics 4, including what it will mean for how you measure marketing activity and conversions, how to get started using GA4 and how to prep your clients for the change.

What is Google Analytics 4?

Google Analytics is a staple tool for marketers to track online activity. If you’ve used Google Analytics in the past, GA4 will look familiar.

So what’s the big difference?

GA4 changes how data is collected and reorients the metrics from sessions to events. This combines users’ web and mobile app data to more seamlessly measure their journey across platforms. GA4’s data collection also takes into account the increasing concerns consumers have around privacy and, in particular, cookie tracking. 

GA4 is currently available (and the default if you set up a new property), but many marketers still rely on Universal Analytics. Additionally, since GA4 is still being updated, everyone is in the same boat, learning how to use the new metrics. Companies that integrate with Google Analytics must update their integrations before the July 2023 deadline, and this includes CallRail. We are currently revamping our Google Analytics integration, so you can continue to report on and analyze call data in Google Analytics and provide more insight into visitor interactions than ever before.

Does GA4 use cookies?

Yes and no.

If you’ve worked in marketing during the past few decades, you know the importance of cookies in helping you measure your goals and advertise your brand. So it might seem jarring to think GA4 is messing with cookies at all.

The short version is that Google Analytics 4 relies on first-party cookies while restricting third-party cookies. GA4 also adds signals to the mix, which is session data from sites and apps that Google associates with users who have signed into their Google accounts and turned on Ads Personalization.

Why is that? Let’s recap what a cookie is first.

Cookies are a way for your computer to remember where you’ve been and what you’ve done on a site and to communicate that back to the site. This makes for a more personalized experience and allows marketers to track engagement.

Third-party cookies are unique because they allow the sites to track users beyond the property. Whole industries grew out of advertising using third-party cookies, but the practice has come under scrutiny from regulators and privacy-conscious consumers. When the European Union’s General Data Protection Regulation (GDPR) took effect in 2018, it kicked off a shift in the way third-party cookies are treated.

By removing support for third-party cookies, GA4 actually beats Google’s browser, Chrome, to the punch. Chrome, the world’s most popular browser, will end third-party cookie support at the end of 2023.

Privacy isn’t the only reason that GA4 is moving away from third-party cookies. As more people use mobile devices to access the internet, more users are foregoing the web in lieu of apps. In fact, in 2021, 90% of mobile time was spent using apps, not the web. That’s a huge shift, and when paired with the death of third-party cookies, it became clear to Google that Universal Analytics wasn’t built for that reality.

GA4 vs. Universal Analytics

Should I use Universal Analytics or GA4?

For now, you have a choice between GA4 and UA. If you’re setting up a new Google Analytics property, it will default to GA4, but you can choose to only use UA through some advanced options during setup.

We recommend using both for now, for several reasons.

Despite being out of its beta, GA4 is still constantly being improved with added features. Moving over now may provide a false sense of what life with just GA4 will really be like.

UA metrics won’t align 1:1 with GA4 metrics. By having both, you can see how your key measurements will be affected by the change and alter your reporting accordingly. For example, if you rely on Bounce Rate to track whether a page is performing well, you’ll lose that in GA4. Instead, you’ll have an Engagement Rate, which cannot be considered the inverse of Bounce Rate because it has a time threshold associated with it.

By waiting to move away from UA, you’ll retain your key integrations with Google Analytics, such as CallRail’s Google Analytics integration.

By leveraging elements of both Universal Analytics and Goole Analytics 4 into your client reporting now, clients will get used to the new system and have time to adjust before transitioning completely to GA4 in 2023.

Ultimately, of course, you’ll be using GA4. But until then, use this time as an opportunity to learn about GA4 without sacrificing your current reports or third-party GA integrations.

What do I gain and lose by upgrading?

With a big change like Google Analytics 4, there are going to be some things that feel like improvements and some things that feel like downgrades. Time will tell what the changes will mean for your business and your clients, but we know the effects of some already.

Here’s what you’ll gain with Google Analytics 4:

  • Event-based tracking: This one could easily go in the “lose” column depending on how you feel about UA’s measurement model of sessions and pageviews. But event-based tracking brings together web and app engagement for a more holistic view of the user, with the potential for richer journey insights.
  • Better reporting and analysis: GA4 borrows from Google Data Studio to provide simple-to-use templates for custom reporting.
  • Automated insights: Artificial intelligence and machine learning are going to highlight new insights for you.

Here’s what you’ll lose when you switch:

  • Historical data: Your historical data in UA (as well as your tags) won’t migrate over to GA4. Since GA4 requires a new property, you’ll essentially be starting from scratch.
  • Your conversions: Since the underlying measurements are changed, your conversions will be different now too.
  • Views: As of now, GA4 doesn’t provide views, which UA users could deploy to configure tests or filter internal traffic from the data.
  • Limits on filters and customer dimensions: IP and hostname filtering have been limited or deprecated and custom dimensions are limited to 50.
  • Third-party integrations: Third-party integrations into GA for everything from your CRMs, to your e-commerce, to your CMS’ that were built on UA’s measurements will no longer work until they’re updated to GA4.

For a full breakdown of everything you need to know before switching to GA4, including what it will mean for the way you measure marketing activity and conversions, how to get started using GA4 and how to prep your clients for the change, download our full guide now. 

See what CallRail’s call tracking can do to enrich your understanding of the customer journey when combined with your web visitor data in Google Analytics. Get started with a free trial today.

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CallRail makes it easy for businesses of all sizes to turn more leads into better customers. Serving more than 200,000 businesses and integrating with leading marketing and sales software, our marketing analytics and business communications solutions deliver real-time insights that help our customers market with confidence.

Wed, 12 Oct 2022 23:49:00 -0500 CallRail en text/html
Killexams : Your guide to Google Analytics 4 attribution

Nowadays, conversion is usually preceded not just by one but several interactions with a website or an app.

Attribution determines the role of each touchpoint in driving conversions and assigns credit for sales to interactions in conversion paths.

As Google’s deprecation of Universal Analytics (UA) nears, it’s crucial to understand attribution in Google Analytics 4 (GA4) – including what is new, what is missing, and what the differences mean for search marketers.

(If you are new to attribution, read the Google Analytics help article on attribution first.)

How Google Analytics 4 attribution works

Universal Analytics reports attributed the entire credit for the conversion to the last click. A direct visit is not considered a click, but for the avoidance of doubt, this attribution model was also called the last non-direct click model. Other attribution models were only available in the Model Comparison Tool in the Multi-Channel Funnels (MCF) reports section.

GA4 offers a wider availability of different attribution models, but it depends on the scope of the report – whether it is the user acquisition source, session source or event source. 

In Universal Analytics, the source dimensions had session scope solely. The MCF reports made it possible to analyze the sources of all sessions on the conversion path. The three scopes of source dimension in GA4 (user, session, event) are the most important and fundamental changes in the attribution area.   

This guide will use the term “source” in a broader meaning as any dimension that indicates the origin of a visit, e.g., channel grouping, source, medium, ad content, campaign, ad group, keyword, search term, etc.

Session source

Session-scope attribution – unsurprisingly – determines the source of the session. It is used, among others, in the Traffic acquisition reports in the Reports section. It works similarly to Universal Analytics in always using the last non-direct click model.

The session source is the source that started the session (e.g., social media referral or organic search result). However, if a direct visit started a session, the session source will be attributed to the source of the previous session (if there was any). 

Quick reminder: A direct visit means that Analytics does not know where the user came from because the click does not pass the referrer, gclid, or UTM parameter.

Therefore, exactly as it was in Universal Analytics, the session source will be direct only if Analytics cannot see any other source of visit for the given user within the lookback window. The default lookback window in GA4 is 90 days, while in Universal Analytics, it was six months by default. We will return to the lookback window matter later in this article.

By the way, what is a session?

A Google Analytics session is not the same as a browser session.

In GA4, a session begins when a user visits the website or app and ends after the user’s inactivity for a specified time (30 minutes by default – see this Analytics help article).

Closing the browser window does not end the session. If the browser window is closed, another visit to the website within the time limit would still belong to the same session – unless the browser deletes cookies and browser data after closing the browser window, for example in incognito mode.

In Universal Analytics, when a user re-visits the website from a new source during an existing session, the existing session is terminated, and a new session starts with that new source. 

In GA4, it is no longer the case. If a visit from a new source occurs during a session, a new session will not start, and the source of the current session will remain unchanged.

It does not mean that the visit from the new source is ignored. GA4 records the source of this visit, and the event-scope attribution reports (more on that later in this article) will take into account all sources of all sessions. (See this Analytics help article.)

A new visit during an existing session may happen, for example, if a user returns from a payment gateway or a webmail site after password recovery or registration confirmation. In GA4, these visits will not artificially inflate the number of sessions, as in Universal Analytics. 

Nevertheless, sources of these visits are so-called unwanted referrals and should be excluded. Visits from excluded referrals are reported as direct visits.

In GA4, these visits are de facto ignored because the session source and the session count remain unchanged. The non-direct attribution modeling in GA4 will assign no credit to this (direct) source (as described later in this article).

In Universal Analytics, the session (regardless of duration) ends at midnight, which is no longer the case in GA4.

First user source 

First user source (source of the first visit) is new to GA4. It shows where the user came from to the website or app for the first time.

It is a part of Google’s new approach to measurement in online marketing, which no longer focuses only on the classic ROAS (revenues vs. costs), but also analyzes the CAC vs. LTV (customer acquisition cost vs. lifetime value).

This approach reflects the app logic: we have to acquire the app user first, and after the app is installed, further marketing efforts engage and monetize the user. However, for the web traffic, it also makes more sense. 

The new customer acquisition goal in Google Ads, available in Performance Max campaigns, also represents a similar approach. In this case, the focus is on the first-time buyer, not the first visit. 

In GA4, the first user visit is recorded by the first_visit event for the website or the first_open event for the app. The naming is self-explanatory.

Therefore, the source of the first visit is a user attribute and indicates where this user’s first visit to the website or application came from.

The first visit source is attributed using the last non-direct click model. Of course, this attribution applies only to interactions before the first website visit or the first open of the app (interactions following the first visit or first open are not taken into account).

Once assigned, the source of the first visit remains unchanged – of course, as long as Google Analytics can technically link the user’s activity on the website and in the app with the same user.

The first user source will be reset if the tracking of the user is lost, for example, if the user does not visit the website for a period longer than the Analytics cookie expiration date.

We will return to the Analytics cookie expiration period and other data collection limitations in GA4 later in this article.

Event scope attribution

In GA4, events replaced sessions as the fundament of data collection and reporting. GA4 makes it possible to report attribution using a selected attribution model for any event (not only for conversions).

The model is set in the Attribution Settings of the GA4 property. There are several pre-defined models to choose from (see the screen below).

Google Analytics 4 Attribution Settings - Pre-defined models.

The default data-driven model can be changed at any time. This change is retroactive (i.e., it will also change the historical data).

A common belief is that Google Analytics 4 no longer uses the last-click attribution model. But is that the case?

In practice, it applies only to customized reports that use event-scope dimensions and metrics, for example, Medium – Conversions.

The default traffic and user acquisition reports use session source and first user source, respectively, and these dimensions use the last click model. It is indicated in the dimension name (e.g., Session – Campaign or First User – Medium).

Remember: source, session source and first user source are three different dimensions where different attribution models apply.

Scope Attribution Model Where available
Session Last click E.g., traffic acquisition reports
User (first user source) Last click E.g., user acquisition report
Event Model set in the GA4 property settings (data-driven by default) E.g., in the Explore section

Attribution settings

The attribution model set in the property settings applies to all reports in the property.

There are several attribution models, known from Universal Analytics (described in the earlier mentioned Analytics help article), to choose from. However:

  • All the models do not assign value to direct visits unless there is no other choice because there is no other interaction on the path. In other words, they all use the non-direct principle, which was not the case in the Universal Analytics pre-defined attribution models, except for the last non-direct click model. 
  • The Ads-preferred models assign the entire conversion value to Google Ads interactions if they occur in the funnel. At the moment, there is only one Ads-preferred model available – the last click model, which is the equivalent of the “last Google Ads click” known from Universal Analytics. In the absence of Google Ads interactions on the funnel, this model works like a regular last-click model.
  • In addition to clicks, models take into account “engaged views” of YouTube ads, that is, watching the ad for 30 seconds (or until the end if the ad is shorter) and other clicks associated with that ad (see this Google Analytics help article for more details).

Again, a change of the attribution model settings works retroactively (i.e., it applies to the historical data before the change). Saved explorations will be recalculated when viewing them.

Lookback window

Google Analytics property settings determine the length of the lookback window. The lookback window determines how far back in time a touchpoint is eligible for attribution credit. The default lookback window is 90 days, but you can change it to 60 or 30 days.

According to Analytics documentation, the lookback window settings apply to all attribution models and all conversion types in Google Analytics 4 (i.e., it also applies to session-level attribution and attribution model comparisons).

The lookback window of the first user source has a separate setting (30 days by default, and it can be changed to 7 days). Are you wondering why it is defined differently? 

Well, first of all, it is worth considering why there is any lookback window for the first visit at all.

Moreover, why are we talking about the first user attribution model, which is always the last (non-direct) click?

After all, GA4 knows the source of the first visit when this visit happens. As it is the first visit, there are no previous visits, and thus no other sources to consider.

So, what is the point of looking deeper in time than the first interaction with a website or app?

The answer is Google Signals. If this option is enabled for the GA4 property in the Data Collection settings, GA4 will enrich the data collected by the tracking code with, among others, information known by Google about logged-in users.

For example, Google may know that the user had an engaged interaction with our YouTube ad on a different device before the first visit.

Similarly, the user may use the app for the first time (first_open) during a direct session, but the install itself may result from a mobile app install campaign in Google Ads, clicked a few days earlier. 

Therefore, if the source of the first visit session is unknown (it is a direct visit), Google Analytics may try to assign the source of the first visit to the earlier known interaction if it occurred during the lookback window period.

In other words, thanks to Google Signals, GA4 may record ad interactions before the first user visit.

Lookback window changes do not work retroactively. It means that they only apply from the moment of the change.

The engaged views of YouTube ads, however, always have three days lookback window, regardless of the property settings.

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It is a nuance but worth noting. Universal Analytics's default lookback window for the acquisition reports was six months, and any change to this period was also non-retroactive. 

Such a change, however, did not apply to conversions but to interactions that had taken place after the change. It reflected the logic of the _utmz cookie, which was responsible for storing the source information.

Its expiration time was set when the cookie was created or updated (i.e., upon a visit from a given source). Universal Analytics no longer uses the _utmz cookie (it was used in earlier versions), but the logic was maintained for data consistency.

For example, changing the lookback window in Universal Analytics from 30 to 90 days did not immediately include interactions from 90 days ago in the acquisition reports for the visits since the date of the change because the virtual "source cookie" for interactions older than 30 days has already "expired."

There was a transition period (in this example, 90 days), after which all conversions were fully reported under the new lookback window. 

Google Analytics 4 uses a different data model, with no continuity with the UA data. They could therefore break with this past and stop using the cookie logic.

For example, they could apply changes to all conversions that have taken place since the change, as it is now in Google Ads. Interpreting such would be much easier. They could, but they did not. 

In GA4, the change applies to interactions still in the lookback window. 

For example, if the lookback window is increased from 30 to 90 days, the conversions will not immediately be reported in the new, 90 days lookback window. It will be reflected in the reports after 60 days from the date of change (the interactions from the initial 30-day lookback window will be remembered).

Reducing the lookback window (e.g., from 90 to 30 days) will apply the change immediately (i.e., all conversions will be reported in the shorter, 30 days window). 

Yes, it sounds exotic. Fortunately, in practice, the analysts do not change the lookback window often. 

The Google Analytics 4 cookie has a standard expiration time of 24 months, but it can be changed to a period between one hour and 25 months (or the cookie may be set as a session cookie and expire after the browser session end).

Subsequent visits may renew this time limit. This will be the period in which Analytics will be able to recognize a returning user and remember the source of the first visit – see this GA4 help article).

However, it does not automatically mean that GA4 will "remember" user data that long.

In addition to the cookie expiration, we also have to deal with the GA4 data retention period. It is set by default to only two months, but you can (and basically, you should) change this setting to 14 months. (In the paid version, Google Analytics 360, it can be up to 50 months.)

After this time, Google deletes user-level data from Analytics servers. To keep this data, you must export it to BigQuery (see this GA4 help article).

It means that reports in the Explore section can only be made within the data retention period (please note that in the Explore section, you cannot select a date range beyond this period).

These restrictions do not apply to standard reports in the Reports section that use aggregated data. GA4 will store this data "forever." 

In the unpaid version of GA4, the first user source data are deleted after 14 months of inactivity. After that, this user will be recorded as a new user.

Therefore, there is no point in, for example, changing the cookie expiration time from default 24 months to a longer period, unless you use Google Analytics 360. 

Conversion export to Google Ads

Exporting conversions to Google Ads is often used as an alternative to the native Google Ads conversion tracking, as the fastest and most convenient way to implement conversion tracking in Google Ads.

However, this time-saving seems illusory in the era of Google Tag Manager. Moreover, this solution has many disadvantages. 

There are several arguments against using imported conversions from Google Analytics to optimize Google Ads. It:

  • Reduces the number of conversions observed in Google Ads.
  • Uses exotic attribution.
  • Is vulnerable to unforeseen Google Analytics configuration and link tagging errors, such as unwanted referrals or redundant UTM parameters.

Therefore, while importing conversions from Analytics may provide interesting data that cannot be collected in Google Ads, using them as goals for optimizing Google Ads campaigns may not be optimal. 

If you import conversions from GA4 to Google Ads, regardless of the GA4 attribution settings, the conversions will be imported using the GA4 last non-direct click model.

This means you will only import conversions whose Google Ads source has not been overwritten by subsequent clicks (e.g., organic search results or social media ads).

Regardless of the property-level attribution settings, Google Analytics allows comparisons of different attribution models in the Advertising section.

Currently, the available models are the same as those available in the property settings, and it is impossible to create custom models. 

Interestingly, GA4 allows reporting in two conversion attribution time methods – interaction time and conversion time (only the latter option was available in Universal Analytics).

The interaction time method is typical for advertising systems, where conversions are attributed to clicks and, thus – costs. It allows a correct match between costs and revenues.

Otherwise, the reports might include conversions after the end of the campaign, in a period when there is no ad spend.

On the other hand, the interaction time method may cause the total number of conversions to change depending on the attribution model, as different models may attribute conversions or their fractions to clicks outside the reporting period.

Moreover, the conversion count and revenue for a given reporting period may grow over time until the lookback window closes.

In other words, we may observe more conversions for the latest period if we look at the same report in the future – which is not the case when conversions are reported in the conversion time.

Both approaches have advantages and disadvantages, so it is good that we can now use both.

Conversion paths report

Compared to Universal Analytics, the GA4 conversion paths report is enriched with additional data: time to conversion and the number of interactions for a given path.

It partly compensates for the lack of time lag and path length reports, which were separate reports in Universal Analytics.

The ability to choose an attribution model for this report may be surprising at first sight.

The attribution model does not affect conversion paths. They remain the same, and their length and time to conversion do not change.

In GA4, the path visualization also includes the fraction of conversion assigned to a given interaction or their series in the selected attribution model.

In the last click model, the last interaction always has a 100% share in the conversion, but in the other models, the distribution will be different.

This feature also allows a better understanding of how the data-driven model worked for the interactions in this report. 

Additional bar graphs are placed above the funnel report, visualizing how the selected attribution model assigned a value to channels at the beginning, middle and end of the funnel.

The early touchpoints are the first 25% of the interactions along the path, while the late touchpoints include the last 25%. The middle touchpoints are the remaining 50% of the interactions. 

If you feel that the distribution between early, middle, and late touchpoints does not look as expected for the multi-touch models, please note that if there are only two interactions, there is one early, one late, and no middle interactions.

If there is only one interaction, for the multi-touch models, it will be reported as late interaction – which distorts these reports the most. 

Probably, it would be better if the only interaction was considered as 33.3% early, 33.3% middle, and 33.3% late interaction.

Thus, the attribution model will only affect the bar charts at the top of the report and the percentages shown in the funnel visualization.

The table figures (funnel interactions, conversions, revenue, funnel length, and time to conversion) will remain the same, regardless of the attribution model.

By default, the conversion paths and model comparison reports include all conversions in the GA4 property. Therefore, it is worth remembering to select the desired conversion first. 

Use of scopes in the reports

Again, the source dimensions in GA4 can have one of three scopes: session, user, and event.

  • In the case of the event scope, the attribution model specified in the property attribution settings is used.
  • The session source (session scope) is assigned to the last non-direct interaction at the session start and remains unchanged for a given session, even if there is a visit from another source during the session. It's the "first source" of the session, although assigned in the last-click model.
  • Similarly, the first user source (user scope) is assigned to the last non-direct interaction before the first visit and remains unchanged.

In Google Analytics, all dimensions and metrics operate within their own scope. For example, the Landing page dimension has the session scope, and the Page dimension has the event scope.

Although technically possible, using dimensions and metrics of different scopes can sometimes lead to confusing or difficult-to-interpret reports.

For example, the Page dimension should be matched with Page views, not Sessions. If we combine Pages with Sessions, Universal Analytics will show the number of sessions similar to Landing page vs. Sessions report.

In GA4, this will be the number of sessions during which a given Page has been visited, and therefore, the sum of sessions for all Pages will be greater than the total number of Sessions.

But if you think about it, there is little point in making such reports – therefore, the uncertain interpretation of these numbers should not worry us too much. 

However, some reports using dimensions and metrics of different scopes will make sense. For example, for source dimensions in GA4:

  • The number of events (event scope) paired with the First user source dimension (user scope) shows how many events were generated by users whose first visit was from a given source.
  • The number of events (event scope) paired with the session source dimension (session scope) shows how many events were generated by users during sessions with a given source.

The GA4 documentation fails to indicate how to interpret the number of sessions or users matched with the event scope. Such explorations, although possible, often contain many not set values.

However, creating such reports doesn't make sense. (See the previously mentioned GA4 help article on scopes.)

Modeled data

Finally, it is worth emphasizing the fundamental change in Google Analytics 4, where reports include data collected by the tracking code enriched with modeled data.

The modeled data uses information collected in the cookieless consent mode for users who have not given consent to tracking and Google Signals data for users logged in to Google. This data is fragmentary, but Google can fill in the missing data using extrapolations and mathematical modeling.

Thanks to Google Signals, in GA4, we can see an approximate but more complete picture of the user's journey.

For example, Universal Analytics recorded an iPhone user who visited the website from a YouTube ad using Safari and never returned.

Universal Analytics also saw a conversion made by another user who came from a direct visit on the Chrome browser for Windows.

Google knows these events belong to the same user because this user was logged into Gmail and YouTube. 

This is how Google Analytics 4, using Signals, can model the cross-device users' behavior. It makes the reported number of users more real (reduces it) and improves the attribution accuracy.

In the example above, the conversion from the direct session can be correctly attributed to the YouTube ad.

Not all users are always logged into Google – many do not even have a Google account.

Therefore, to make the picture more complete, Google Analytics will assume that users who are not logged in behave similarly.

Consequently, GA4 sometimes will supplement the missing sources (e.g., assign certain sources to conversions that were previously assigned to direct).

The behavior of users who have not given consent to tracking is estimated similarly.

Analytics knows the number of page views and conversions from the non-consented users and can model how many users generated these pageviews and conservatively attribute conversions to sources.

Enriching Analytics data with Google Signals may take up to a week. Therefore, the latest data may change in the future.

Please note that we also dealt with delays in Universal Analytics, where most reports could have delays of up to 48 hours.

Various privacy-oriented technology solutions, such as PCM by Apple or similar solutions proposed by Google (the Privacy Sandbox), randomly delay conversion reporting by 24-48 hours.

Therefore, we must get used to the fact that the full view of analytical data will only be available after some time. 

In GA4, we can also enhance the reports using the 1st party data, namely the User-ID.

This feature was also available in Universal Analytics, but the separate "User-ID View" included the "logged-in" sessions with User-ID solely and, honestly, wasn't that useful.

GA4 reports combine the User-ID data with the Client-ID (the Analytics cookie identifier) and Google Signals, which makes the data more complete, especially in the cross-device aspect and LTV measurement. 

The complexity of these processes may cause greater or lesser discrepancies between the data in different reports.

We should get used to it, but hopefully, as GA4 recovers from childhood illnesses, these discrepancies will become less and less significant.

It is worth remembering that Google Analytics is not accounting software.

Its objective is not to record every event with 100% precision but to indicate trends and support decision-making – for which approximate data is sufficient.

Author's note: This article was written using Google help articles, answers given by Analytics support and results from my experiments. 

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

New on Search Engine Land

About The Author

Founder and CEO of Adequate Interactive Boutique, awards-winning marketing consultancy. Certified Google Ads and Analytics specialist since 2007. Author of numerous publications, conference speaker, and university lecturer. Expert in measurement, attribution, and profit-driven media optimization.

Wed, 12 Oct 2022 06:53:00 -0500 Witold Wrodarczyk en text/html
Killexams : Tips and Tools to Help Students Study, Take Notes, and Focus

With a new academic year rolling around, students of all ages will be looking for help and guidance with their work—and there are a wealth of options on mobile app stores and the web to help you succeed.

Here we've picked out some of the best apps and services across multiple categories, including time management, homework help, note-taking, and more. Put them together and you've got a comprehensive toolkit for making sure that this year is a good one.

No matter what your requirements, courses, or study habits are, there should be something here for you (or for the young student in your life). You might be surprised at just how much difference the right app can make.


Trello can adapt itself to whatever purpose you have in mind.

Courtesy of Trello

The main appeal of Trello is its versatility: You can adapt the simple card-based interface in whichever way you want—whether to keep track of individual homework assignments or to log multiple research strands in an essay—and the software will adapt accordingly.

You can assign categories and deadlines to cards, attach files to them, and drop in to-do lists. However you decide to use Trello, you're going to find it straightforward to get around the app with easy drag-and-drop operations and a ton of options and features.

Trello (freemium for web, Android, iOS)


Get help from Socratic with just about any topic.

Courtesy of Socratic

Powered by Google's artificial intelligence engines, Socratic is here to answer any question on any topic, whether you need step-by-step math explanations, a quick overview of a historical event or work of literature, or details of a particular set of biological processes.

Sun, 09 Oct 2022 01:00:00 -0500 en-US text/html
Killexams : A Guide To Google’s Knowledge Graph Search API For SEO

Google introduced the Knowledge Graph in 2012 to help searchers discover new information quicker.

Essentially, users can search for places, people, companies, and products and find instant results that are most relevant to the query.

The Knowledge Graph is a collection of topics, also known as entities, connecting to other entities. Entities are single information objects that can be uniquely defined.

They enable Google to go beyond just keyword matching when returning a response to a particular query. This is further helping Google towards its goal of becoming an answer engine.

Google will show Knowledge Graph data within SERP features such as knowledge panels, knowledge cards, and featured snippets.

This can help brands become more visible in search results and build authority for certain topics. Structured data on websites can influence data pulled into the Knowledge Graph.

Google uses the Knowledge Graph to provide a better search experience for users as it can better understand different syllabus and their relationships to each other.

For example, if we want to see a film’s cast, Google can display this in a carousel format on the search results page.

However, these SERP (search engine results page) features can also lead to fewer website clicks, as Google can show much more information on the search result page.

This enables them to deliver a fast and accurate response for searchers and direct them towards other search results, with features such as “People also search for” and relevant queries related to the main search term.

For example, if we take the K-pop group BTS, within a single search, I can see a list of all of the members, their songs and albums, as well as upcoming events, awards they have won, and the different places I can listen to their music.

All in one search without having to visit a single external website.

The Knowledge Graph API

The Knowledge Graph API, which Google has built, enables us to find entities within the Google Knowledge Graph for certain queries.

It gives us direct access to the database to see the entities marked up for each query. It is also independent of the user’s location, providing a more accurate view of the Knowledge Graph.

Some example use cases of the API, as given by Google, include:

  • Getting a ranked list of the most notable entities that match certain criteria.
  • They are predictively completing entities in a search box.
  • Annotation/organizing content using the Knowledge Graph entities.

As the documentation states, the API itself returns only individual matching entities rather than graphs of interconnected entities.

Using Python To Call The API

There are four different clients that Google enables the API to be called via: Python, Java, JavaScript, and PHP.

An example starting point for each can be found on the relevant page in the documentation.

For this example, I will use Python as it is the language I am most familiar with.

Creating An API Key

The first step is creating an API key to send a request to the API.

To generate an API key, go to the Google API console and navigate to the credentials page.

The next step is to go to the API library, search for Knowledge Graph, and then enable it.

You can save a note of your API key, but you are also able to easily find the API key again by clicking on the API that you had already generated.

A Simple API Request

To return entities matching a query, together with the results score for each entity, there is a simple piece of Python code that you can run, either in Google Colab (easily accessible for beginners) or in your local environment.

api_key = ' ' #add your API key
query = 'BTS' #add your query
service_url = ''
params = {
'query': query,
'limit': 10,
'indent': True,
'key': api_key,
url = service_url + '?' + urllib.parse.urlencode(params)
response = json.loads(urllib.request.urlopen(url).read())
for element in response['itemListElement']:
print(element['result']['name'] + ' (' + str(element['resultScore']) + ')')

This will produce an outcome like the below:

Within this, we can set a couple of parameters, depending on what we’re looking for.

The first thing you will need to add is your API key, followed by the query for which you would like to generate the results.

The parameters are then set to call the API key you have already added and the query you have selected.

This enables you to easily change the query you are searching for each time you run the code.

Then we have the limit, which is the number of entities you want to return. The default for this is 20, with a maximum of 500. Remember that requests with high limits have a higher chance of timing out.

Then we can use a Boolean (True or False) to decide whether we want to indent the JSON response for easy formatting.

There are other parameters that you can include, such as:

  • Languages: a list of the language codes to which you want to limit the response.
  • Types: used to restrict the entities to those of the type you choose, e.g., if you only want ‘Person’ entity results.

We then ask the script to call the URL, complete the request and parse the result to a simple print of the entity name and result score for each entity, which will be enclosed in parenthesis.

Extracting Even More

Returning the entities and their result score is just scratching the surface. There is so much more we can get from the Knowledge Graph API.

We can return a JSON object containing all the response fields stored for each entity with a few more lines of code and some functions.

First, we want to request a return of the session’s page that will be searched through the API.

def get_source(url):
session = HTMLSession()
response = session.get(url)
return response
except requests.exceptions.RequestException as e:

Then, using a similar API request as in the original code, we can call it in conjunction with our query request using the same parameters.

def knowledge_graph(api_key, query):
query = 'BTS' #add your query
service_url = ''
params = {
'query': query,
'limit': 10,
'indent': True,
'key': api_key,
url = service_url + '?' + urllib.parse.urlencode(params)
response = get_source(url)

Then, we enter our API key to return our response object with the full data.

return json.loads(response.text)
api_key = " " #add your API key
knowledge_graph_json = knowledge_graph(api_key, query)

To see the results a little easier and help make more sense of the response, we can normalize the JSON object into a Pandas DataFrame. This will take each field and transfer it into a column, with each entity a different row.

pd.json_normalize(knowledge_graph_json, record_path=’itemListElement’)

A Guide To Google’s Knowledge Graph Search API For SEO

I also found it interesting to run this code on different days with the same query and review how the results change.

Response Fields

Several fields will be extracted for each entity within the Knowledge Graph API:

  • id: the canonical URI for the entity.
  • name: the name of the entity.
  • type: a list of supported schema types that match the entity.
  • description: a short description of the entity.
  • image: an image that is related to the entity.
  • detailedDescription: a detailed description of the entity.
  • url: the official website of the entity.
  • resultScore: An indicator of how well the entity matches the query.

The id field refers to the MID (machine-generated identifier), a unique identifier for each entity.

This typically starts with kg:/m/ followed by a short appended string. MIDs break down human language into a format that machines can understand.

These MIDs also match the entity in Google Trends and can also be used to retrieve the URL of each entity, even if there is no knowledge panel for it.

Confidence Score

The resultScore (also known as confidence score) represents Google’s confidence in its understanding of the entity. It’s essentially the perceived strength of the relationship between the entity that Google has recognized for the query, and the entity that has been returned.

The higher the result score, the more confidence Google has in the entity being the best match for the query.

However, there is no certain that the entity with the highest score will appear as the featured snippet in the search results.

This score, particularly, is useful when analyzing different queries for opportunities.

For example, suppose you notice low constituency scores for a particular query. In that case, this highlights the opportunity to optimize pages to overtake the identified pages for the entity.

The URL for the entity is also considered the “entity home,” which is the website and page that Google recognizes as the most authoritative source with the most accurate information about the entity.

To Improve the confidence score, ensuring that your website is consistent with the information on the entity’s home is important.

Improving the quality and detail provided on a webpage will also help Improve the confidence score, pairing this with PR activity to further enhance the website’s authority for the chosen entity topic.

Extracting Insights

You can do several things with your Knowledge Graph response findings, including identifying areas of opportunity and reviewing current entities and entity homes for particular queries.

For example, ensuring you have the most appropriate schema markup and on-page optimization to connect with your target entities is an important first step.

Keyword Research

When completing keyword research, it is worth considering whether your current targeting makes sense if a strong entity exists for a particular keyword.

After all, Google’s overarching goal is to provide the most useful information in search results. With zero-click search increasing, competition for search terms and the ability to appear in SERP features is also increasing.

Brand Building

Using entities is an excellent way to build a brand or company’s organic search presence and authority in a particular space.

It’s useful to know the entities behind a certain query. They can provide us insights around the search insight for keywords and make it even easier to create authoritative, helpful content in line with this.

Competitor Research

As the API provides a ranked list of entities that appear for queries, you can view a high level of insights rather than performing numerous searches to see what appears.

This will enable you to review your competitors’ performance for particular queries and how you compare.

You can also use these insights to ensure you can increase your confidence score to overtake competitors in the results.

The API allows you to keep track of this regularly and report on any changes you see, potentially before any SERP features change.

In Summary

I hope this has provided you a place to start with analyzing the Knowledge Graph and extracting valuable insights to help optimize your appearance in search features.

As Google explains, the Knowledge Graph is used to enhance Google search to find the right thing, get the best summary, and go deeper and broader.

Being able to see under the hood of the Knowledge Graph is a great place to start to ensure your website is the best source for Google to use to do just that.

I have created a Google Colab notebook here for you to use and play around with the code.

I’d love to know what insights you have extracted for your queries. (Please remember to make a copy and to add your own generated API).

You can also find a version of the code on GitHub here.

More resources:

Featured Image: REDPIXEL.PL/Shutterstock

Thu, 13 Oct 2022 00:49:00 -0500 en text/html
Killexams : Step-By-Step Guide on Google Nest Hello Installation

Time:An hour or less



Is the Google Nest Hello Compatible with Your Doorbell?

Before you get started, before you even buy one, the first step is to make sure that your current doorbell is compatible with the Google Nest Hello Video Doorbell. The good news is that almost all wired doorbells can be replaced with the Nest Hello. Here's how to check to make sure your setup will work:

  • Ring your doorbell to locate your chime box.
  • Remove the cover and look inside. What kind of doorbell chime do you have?
    • An electronic chime has batteries and wires, which are compatible with Google Nest Hello.
    • A mechanical chime has only wires, also compatible with Google Nest Hello.
    • A wireless chime with batteries and NO wires is NOT Compatible with Google Nest Hello unless you purchase a power adapter.
  • Locate the transformer
    • The wires leading out of your chime box connect to the transformer.
    • Look for the transformer in the basement, the attic, or around the circuit breaker box.
  • Check the transformer voltage
    • The voltage rating is printed on the transformer.
    • Google Nest Hello doorbell requires transformer voltage between 16 and 24 volts AC.
      • Pro tip: Do the compatibility check before purchasing the Google Nest Hello.

Please be careful in this step. Electrical currents are present. DO NOT touch any wires!


Sign In to Nest

Add Product & Scan the QR Code

  • In the Nest app touch the + sign to add the product to your app.
  • Scan the QR code located on the back of the Google Nest Hello with your phone or tablet's camera.
    • Note: There is a QR code on the envelope in the box as well.

Cut the Power

  • Turn off the power to your doorbell and chime at your home's breaker box.

Chime Terminals & Labels

  • Your doorbell chime may have terminals that are labeled FRONT, REAR, and TRANS.
    • FRONT is for your front doorbell.
    • REAR is for the back doorbell.
    • TRANS is for the transformer.
  • Change out the terminal for the doorbell you are replacing.

Disconnect Front Wire

  • Disconnect the FRONT wire from the terminal on the chime.
  • Straighten the wire and trim it so you see 1/4-in of bare wire.

Attach Front Wire

  • Pinch the plastic clip and insert the FRONT wire into the clip on the chime connector's WHITE wire.
  • Connect the chime connector’s WHITE wire to the terminal on your doorbell chime labeled FRONT.

Disconnect Trans Wire

  • Disconnect the TRANS wire from the terminal on the chime.
  • Straighten the wire and trim it so you see 1/4-in of bare wire.
  • Pinch the plastic clip and insert the TRANS wire into the clip on the chime connector's GRAY wire.
  • Connect the chime connector’s GRAY wire to the terminal on your doorbell chime labeled TRANS.

Attach the Chime Connector

  • Find a good place to stick the chime connector, making sure the wires don’t interfere with the chimes.
    • Note: You may have to put the chime connector outside the chime box.

Remove the Old Doorbell

  • Unscrew the two screws and remove the old doorbell.
  • Disconnect the wires from the back of the doorbell.
  • Bend or tape the wires so they don't fall back into the hole.

Install the Wall Plate

  • Pull your wires through the center hole of the wall plate.
  • Position the wall plate so the wires come through the bottom half of the wall plate hole.
    • Note: If the wires don’t come through, they can prevent Google Nest Hello from locking onto the wall plate.
  • Mark the screw holes through the wall plate, making sure the Nest logo is at the bottom.
  • Drill two 3/32-in pilot holes at your marks.
    • Pro tip: If drilling into brick or stucco, use the included masonry bit.
  • Attach the wall plate using the two screws provided. The bottom horizontal screw hole enables you to straighten the wall plate.

Install Google Nest Hello

  • Connect the two wires to the screw terminals on the back of the Google Nest Hello, pointing the wires down and pushing any excess wire back into the hole.
  • Attach the doorbell to the wall plate by sliding the top of the doorbell into the plate, then click it into the bottom of the plate.
    • You can remove the Google Nest Hello doorbell by using the tool included.
    • You can change the angle of the Google Nest Hello camera by using the included wedge.

Power Up

  • Switch the power back on at the breaker box.
  • The Google Nest Hello should have a blue ring around the doorbell button.

Connection to WiFi

  • Tell the Nest app where you installed the Google Nest Hello doorbell (i.e., front door, back door, etc.).
  • Select your home WiFi network and enter your password.
    • Note: If you already have a Nest product installed in your home, Google Nest Hello will attempt to connect to your WiFi from it.
  • Google Nest Hello should connect to your WiFi.

Test the App

  • Test the app by ringing the doorbell to check that your doorbell chime is working.
    • Note: You should also get a notification from the app that someone's at your door.
Wed, 12 Oct 2022 12:53:00 -0500 en-US text/html
Killexams : Google Pixel 7 upgrade guide — here’s who should get the new Pixel

A pair of new Google phones has arrived, and maybe you're considering picking up either model, especially in light of the positive reviews the Pixel 7 and Pixel 7 Pro are getting. Google's latest handsets have a lot to offer, chiefly an updated Tensor chipset that builds upon the smart features and upgraded photography last year's models offered. Even better, Google held the line on pricing, so you're getting premium features for less than you'd pay for many of the leading flagships.

That's an attractive formula for a lot of smartphone shoppers, especially those who have already bought a Pixel phone and have come to appreciate how great Google's handsets are at taking pictures. With two Pixel 7 models now available, current Pixel owners will need to ask themselves whether it's time to upgrade to a new Google device.