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Exam Code: AI-102 Practice test 2022 by team
AI-102 Designing and Implementing a Microsoft Azure AI Solution

Exam Number: AI-102
Exam Name : Designing and Implementing a Microsoft Azure AI Solution


The Cisco Customer Success Manager (DTCSM) v2.1 course gives you the confidence and competence to fulfill the Customer Success Manager (CSM) role successfully, helping your customers realize value from their solutions and achieve their business outcomes. The course offers experiential learning through practical exercises using situations based on real-life use cases and case studies. In this highly interactive course, you can practice and gain confidence in fulfilling core tasks using best-practice tools and methodologies while receiving feedback from the facilitator and your peers.

Candidates for the Azure AI Engineer Associate certification should have subject matter expertise building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.

Azure AI Engineers work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.

Candidates for this certification should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.

They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principle.

Plan and manage an Azure Cognitive Services solution
Implement Computer Vision solutions
Implement natural language processing solutions
Implement knowledge mining solutions
Implement conversational AI solutions

Plan and Manage an Azure Cognitive Services Solution (15-20%)
Select the appropriate Cognitive Services resource
 select the appropriate cognitive service for a vision solution
 select the appropriate cognitive service for a language analysis solution
 select the appropriate cognitive Service for a decision support solution
 select the appropriate cognitive service for a speech solution
Plan and configure security for a Cognitive Services solution
 manage Cognitive Services account keys
 manage authentication for a resource
 secure Cognitive Services by using Azure Virtual Network
 plan for a solution that meets responsible AI principles
Create a Cognitive Services resource
 create a Cognitive Services resource
 configure diagnostic logging for a Cognitive Services resource
 manage Cognitive Services costs
 monitor a cognitive service
 implement a privacy policy in Cognitive Services
Plan and implement Cognitive Services containers
 identify when to deploy to a container
 containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics, Speech, Form Recognizer)
 deploy Cognitive Services Containers in Microsoft Azure
Implement Computer Vision Solutions (20-25%)
Analyze images by using the Computer Vision API
 retrieve image descriptions and tags by using the Computer Vision API
 identify landmarks and celebrities by using the Computer Vision API
 detect brands in images by using the Computer Vision API
 moderate content in images by using the Computer Vision API
 generate thumbnails by using the Computer Vision API
Extract text from images
 extract text from images or PDFs by using the Computer Vision service
 extract information using pre-built models in Form Recognizer
 build and optimize a custom model for Form Recognizer
Extract facial information from images
 detect faces in an image by using the Face API
 recognize faces in an image by using the Face API
 analyze facial attributes by using the Face API
 match similar faces by using the Face API
Implement image classification by using the Custom Vision service
 label images by using the Computer Vision Portal
 train a custom image classification model in the Custom Vision Portal
 train a custom image classification model by using the SDK
 manage model iterations
 evaluate classification model metrics
 publish a trained iteration of a model
 export a model in an appropriate format for a specific target
 consume a classification model from a client application
 deploy image classification custom models to containers
Implement an object detection solution by using the Custom Vision service
 label images with bounding boxes by using the Computer Vision Portal
 train a custom object detection model by using the Custom Vision Portal
 train a custom object detection model by using the SDK
 manage model iterations
 evaluate object detection model metrics
 publish a trained iteration of a model
 consume an object detection model from a client application
 deploy custom object detection models to containers
Analyze video by using Video Indexer
 process a video
 extract insights from a video
 moderate content in a video
 customize the Brands model used by Video Indexer
 customize the Language model used by Video Indexer by using the Custom Speech service
 customize the Person model used by Video Indexer
 extract insights from a live stream of video data
Implement Natural Language Processing Solutions (20-25%)
Analyze text by using the Text Analytics service
 retrieve and process key phrases
 retrieve and process entity information (people, places, urls, etc.)
 retrieve and process sentiment
 detect the language used in text
Manage speech by using the Speech service
 implement text-to-speech
 customize text-to-speech
 implement speech-to-text
 Strengthen speech-to-text accuracy
 Strengthen text-to-speech accuracy
 implement intent recognition
Translate language
 translate text by using the Translator service
 translate speech-to-speech by using the Speech service
 translate speech-to-text by using the Speech service
Build an initial language model by using Language Understanding Service (LUIS)
 create intents and entities based on a schema, and then add utterances
 create complex hierarchical entities
o use this instead of roles
 train and deploy a model
Iterate on and optimize a language model by using LUIS
 implement phrase lists
 implement a model as a feature (i.e. prebuilt entities)
 manage punctuation and diacritics
 implement active learning
 monitor and correct data imbalances
 implement patterns
Manage a LUIS model
 manage collaborators
 manage versioning
 publish a model through the portal or in a container
 export a LUIS package
 deploy a LUIS package to a container
 integrate Bot Framework (LUDown) to run outside of the LUIS portal
Implement Knowledge Mining Solutions (15-20%)
Implement a Cognitive Search solution
 create data sources
 define an index
 create and run an indexer
 query an index
 configure an index to support autocomplete and autosuggest
 boost results based on relevance
 implement synonyms
Implement an enrichment pipeline
 attach a Cognitive Services account to a skillset
 select and include built-in skills for documents
 implement custom skills and include them in a skillset
Implement a knowledge store
 define file projections
 define object projections
 define table projections
 query projections
Manage a Cognitive Search solution
 provision Cognitive Search
 configure security for Cognitive Search
 configure scalability for Cognitive Search
Manage indexing
 manage re-indexing
 rebuild indexes
 schedule indexing
 monitor indexing
 implement incremental indexing
 manage concurrency
 push data to an index
 troubleshoot indexing for a pipeline
Implement Conversational AI Solutions (15-20%)
Create a knowledge base by using QnA Maker
 create a QnA Maker service
 create a knowledge base
 import a knowledge base
 train and test a knowledge base
 publish a knowledge base
 create a multi-turn conversation
 add alternate phrasing
 add chit-chat to a knowledge base
 export a knowledge base
 add active learning to a knowledge base
 manage collaborators
Design and implement conversation flow
 design conversation logic for a bot
 create and evaluate *.chat file conversations by using the Bot Framework Emulator
 choose an appropriate conversational model for a bot, including activity handlers and dialogs
Create a bot by using the Bot Framework SDK
 use the Bot Framework SDK to create a bot from a template
 implement activity handlers and dialogs
 use Turn Context
 test a bot using the Bot Framework Emulator
 deploy a bot to Azure
Create a bot by using the Bot Framework Composer
 implement dialogs
 maintain state
 implement logging for a bot conversation
 implement prompts for user input
 troubleshoot a conversational bot
 test a bot
 publish a bot
 add language generation for a response
 design and implement adaptive cards
Integrate Cognitive Services into a bot
 integrate a QnA Maker service
 integrate a LUIS service
 integrate a Speech service
 integrate Dispatch for multiple language models
 manage keys in app settings file

Designing and Implementing a Microsoft Azure AI Solution
Microsoft Implementing information source
Killexams : Microsoft Implementing information source - BingNews Search results Killexams : Microsoft Implementing information source - BingNews Killexams : Empower Your Security Operations Team to Combat Emerging Threats

The implementation of defense-in-depth architectures and operating system hardening technologies have altered the threat landscape. Historically, zero-click, singular vulnerabilities were commonly discovered and exploited. The modern-day defensive posture requires attackers to successfully chain together multiple exploit techniques to gain control of a target system. The increased utilization of dynamic analysis systems has driven attackers to evade detection by requiring input or action from the user. Sometimes, the victim must perform several manual steps before the underlying payload is activated. Otherwise, it remains dormant and undetectable through behavioral analysis.

It is well known that client-side attacks are the predominant access vector for most initial access. Web browser and email-based malware campaigns target users through phishing, social engineering, and exploitation. Productivity and business tools from vendors like Adobe and Microsoft are widespread and provide attackers with many options. Combining the lack of security awareness training and well-developed social engineering tactics frequently results in users permitting the execution of malicious embedded logic like weaponized macros or other scripts. Analysis of these common malware carriers is time-consuming and tedious, and it requires expert skills. To adequately prevent, detect, and respond to these threats, an organization must throw everything at the problem and augment this previously human-intensive process.

Deep File Inspection (DFI) is one approach to ease the burden associated with continuous security monitoring. DFI is a static-analysis engine that inspects beyond Layer 7 of the OSI model, essentially automating the work of your typical SOC analyst or security researcher. Regardless of the complexity of evasive techniques a threat actor utilizes, DFI dissects malicious carriers to expose embedded logic, semantic context, and metadata. Coercive graphical lures are extracted and processed through a machine vision layer, adding to the semantic context of the original file. Commonly used obfuscation methods and encoding mechanisms are automatically discovered and deciphered.

A public concern that SOC analysts, IR teams, and security researchers encounter is the limited availability of context for detection analytics. In the case of intrusion prevention systems, resources are limited to microseconds of time and kilobytes of analyzable data. Intrusion detection systems can typically dig deeper, taking additional milliseconds to expose further data.

Regarding the time-analysis trade-off, the next step up is behavioral monitoring or sandboxed execution. This class of solutions detonates samples in a virtualized environment and annotates the system's behavior for threat detection; this process is both compute- and time-intensive, taking minutes to analyze each file. There is a middle ground where a few additional seconds can provide previously unseen detection opportunities.

An example of an evasive threat is the latest TA570 campaigns that delivered Qbot malware with thread-hijacked emails. This wave of malspam utilized two different methods to provide the payload. The first method used a shortcut LNK to run a DLL with the hidden attribute. The second method is a Word document using the Follina (CVE-2022-30190) exploit.

Recent Qbot threat sequence
Figure 2. latest Qbot threat sequence. Source: InQuest

The attached HTML file contains an antiquated JS function to convert the embedded base64 string into a zip archive and prompt the victim to download. When extracted, the zip file contains a disk image that will be mounted showing either a shortcut or the shortcut and word document. The shortcut will execute the Qbot DLL within the directory with the hidden attribute set. At the same time, the Word file will attempt to exploit the MSDT vulnerability and download the payload from a remote server.

It is challenging for many solutions to carve the zip archive from the encoded HTML, extract the IMG file, and identify the weaponized contents. In this example, multiple threat signatures are seen from ambiguities within the SMTP headers, down to the hidden DLL or Follina document.

Variety of signatures alerts.
Figure 3. Variety of signatures alerts. Source: InQuest

Another interesting approach for detection is the concept of retrospective analysis or RetroHunting. While DFI creates a new dimension of data, RetroHunting provides a new dimension of time for analyzing historical events. The appearance of Follina and other zero-day vulnerabilities illustrates the usefulness of this capability by facilitating the detection of previously unseen alerts with emerging threat intelligence and detection logic.

In addition to a library of predeveloped signatures, analysts can develop user-defined YARA rules to combine strings, bytes patterns, and regular expressions via flexible conditional logic.

When confronted with novel attack techniques being encountered in the wild, security leaders must provide an opportunity to empower your detection operations and overcome the limitations inherent with other malware prevention solutions. A free resource to test the efficacy of a mail provider's security controls is the Email Security Assessment.

About the Author

Josiah Smith

Josiah Smith has almost a decade of experience in the realm of security. Before becoming a threat engineer, Josiah worked as a cyber operator, overseeing signature management and host-based detection programs. He spent several years in a room without windows, focusing on network detection, threat hunting, and IR investigations. He began his career as a member of the US Air Force, and most of his experience is with the DoD.

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Killexams : How machine learning AI is going to change gaming forever

Over the past few days, I've watched my productivity drop (don't tell my boss) a fairly huge amount owing to a sudden explosion of consumer-friendly and highly memeable artificial intelligence (AI) machine learning (ML) tools you might have seen on social media.

Services like Craiyon that automatically generate rough AI images based on virtually any input prompt have produced a range of wild and wacky collages across the net, which you can peruse yourself over on subreddits like /r/weirddalle or Twitter accounts like @Dalle2Pics