PCEP-30-02 questions - PCEP ? Certified Entry-Level Python Programmer Updated: 2024

Just this summer, the Nintendo Entertainment System had its 35th release anniversary, and even after years of discontinuation, it is still going strong in the hacker community. Exhibit A: [Matthew Earl]. For one of his upcoming projects, [Matthew] needed to get his hands on the background images of the NES classic Super Mario Bros. Instead of just getting some ready-rendered images and stitching them together, he decided to take care of the rendering himself, once he extracts the raw game data.

Since there is no official source code available for Super Mario Bros, [Matthew] used a disassembled version to get started looking for the image data. To avoid studying through thousands of lines of assembly code, and to also see what actually happens during execution, he wrapped the game’s ROM data into py65emu, a Python library emulating the 6502, the CPU that drives the NES. By adding a simple wrapper around the emulator’s memory handler that tracks reads on uninitialized data, [Matthew] managed to find out which parameters he needs to feed to the parser routine in order to get the image tile data. After an excursion into the Picture Processing Unit (PPU) and its memory arrangements, [Matthew] had everything he needed to create the Python script that will render the game background straight from its ROM data.

Even if extracting NES game data is not your thing, the emulator concept [Matthew] uses might be still worth a read. On the other hand, if you want to dig deeper into the NES, you should definitely have a look at emulating an SNES game on a NES, presented on the NES itself.

A common theme in The Hackaday Prize and Hackaday.io in general is tools to make more tools. There are a lot of people out there trying to make the next Bus Pirate, and simply measuring things is the first step towards automating a house or creating the next great blinkey invention.

In what is probably the most capable measurement system in the running for this year’s Hackaday Prize, [jithin] is working on a Python Powered Scientific Instrumentation Tool. It’s a microcontroller-powered box containing just about every imaginable benchtop electronics tool, from constant current supplies, LCR meters, waveform generators, frequency counters, and a logic analyzer.

This project is stuffed to the gills with just about every electronic tool imaginable; there are programmable gain amplifiers, voltage references, DACs and constant current sources, opamps and comparators, all connected to a bunch of banana jacks. All of these components are tied up in a nifty Python framework, allowing a bunch of measurements to be taken by a single box.

If that’s not enough, [jithin] is also working on wireless extension nodes for this box to get data from multiple acquisition points where wires would be unfeasible. This feature uses a NRF24L01+ radio module; it’s more than enough bandwidth for a lot of sensors, and there’s enough space all the wireless sensors you would ever need.

In an era where artificial intelligence (AI) is reshaping landscapes across industries, understanding and leveraging this revolutionary technology is no longer a luxury but a necessity.

However, the journey into AI can often seem daunting, especially for those who don't come from a background steeped in math or coding. This notion, though common, is a misconception that needs addressing. AI, in its essence, is an inclusive field, open to enthusiasts from diverse backgrounds, be it marketing, healthcare, arts, or finance.

AI proficiency is becoming crucial in the job market. According to Morgan Stanley, AI will impact 40% of jobs within three years. An Amazon survey reveals 75% of employers struggle to find AI-skilled candidates.

Forrester predicts 60% of employees will integrate personal AI tools into their work by 2024. This surge in AI demand will likely drive intense upskilling, making AI familiarity as common as Excel skills in white-collar job resumes.

LinkedIn's Emerging Jobs Report identified AI specialists as the top emerging job role, with a 74% annual growth rate. This reflects the increasing importance of AI skills across various industries.

From understanding AI's fundamental concepts to hands-on projects and networking in AI communities, this journey is more than just acquiring a skill—it's about embracing a future where AI is as ubiquitous as the internet. Are you ready?

So, let's demystify AI and unlock its potential together, making it not just a buzzword on your LinkedIn profile but a tangible asset in your career and leader progression.

Lay the Foundation with AI Basics:

Begin your journey by immersing yourself in the fundamental concepts of AI. Utilize accessible online resources such as articles, podcasts, and YouTube videos. Focus on materials that simplify complex courses like machine learning, natural language processing, and neural networks. This foundational knowledge is your stepping stone into the world of AI.

Enroll in Beginner-Friendly Online Courses:

Platforms like Coursera, edX, or Udemy offer entry-level courses in AI and machine learning. These courses are designed for beginners, easing you into the subject matter without overwhelming you with technical jargon or advanced math. It's a structured way to build your AI knowledge from the ground up.

Acquire Basic Programming Skills:

Familiarize yourself with the basics of programming, particularly in Python, which is widely used in AI applications. You don’t need to aim for expert-level coding skills; just a functional understanding will suffice. Look for beginner-level courses that cater to those new to programming. This skill will be invaluable as you delve deeper into AI.

Discover AI Applications in Your Field:

Investigate how AI is revolutionizing your industry or area of interest. This exploration makes your learning journey relevant and engaging. Whether you’re in healthcare, finance, the arts, or any other sector, understanding how AI is applied in your field will inspire and inform your own journey.

Engage Actively in Practical AI Learning:

• Workshops and Webinars: Attend AI workshops and webinars, many of which are offered for free or at a low cost, and many are online. These events are not only educational but also provide opportunities to network with AI professionals and enthusiasts.
• Hands-On Projects: Get practical experience by participating in real-world AI projects on platforms like Kaggle. This hands-on approach helps solidify your learning and gives you a taste of real AI problem-solving.
• Join AI Communities: Become a part of AI-focused forums and social media groups. These communities are great for asking questions, sharing knowledge, and staying updated with the latest AI trends.
• Stay Informed and Add to Your Credentials: Regularly read AI-related books and follow AI news on platforms like TechCrunch and Wired. As you progress, update your LinkedIn profile with new skills, course certifications, and project experiences. If your interest in AI deepens, consider enrolling in comprehensive certification programs for more formal recognition of your expertise.

McKinsey's research estimates that by 2030, as many as 375 million workers, or roughly 14% of the global workforce, may need to switch occupational categories due to AI and automation. Forrester Research predicts that by 2030, the job market will lose 29% of jobs to automation but will also gain 13% new jobs created due to AI, leading to a net loss of 16% of jobs.

Adaptability and skills development are key factors in preparing for the evolving job market influenced by AI advancements. Good luck, and may your AI path be filled with exciting discoveries and learning opportunities!

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While debates rage around the pros and cons of artificial intelligence, the technology is slowly being adopted by the online retail sector and generating some positive results. It has the potential to do so much more, though. While many people talk about AI, it’s fair to say that few fully understand its potential or how to realize that in their own e-commerce operations.

The AI in retail market size is expected to grow from $7.3 billion in 2023 to more than $29 billion by 2028, a compound annual growth rate of more than 30 percent. Yet a new report — albeit covering more sectors than retail — shows that while almost all companies surveyed said the urgency to deploy AI-powered technologies has increased, only 14 percent are fully ready to integrate AI into their business.

Although the rapid take-up of large language model (LLM) solutions such as ChatGPT, BERT, and Microsoft’s Turing have put AI technology within reach of anyone with internet access, unless it's properly implemented in a business environment, its effectiveness will be limited. It's also important to note that without the right skills and knowledge, serious regulatory and privacy issues can arise.

LLMs are capable of executing a broad range of natural language processing (NLP) tasks, but they are generalists, not specialists. For a business to begin to benefit from integrating AI into its operations, the primary goals of using a LLM, the types of tasks it's required to perform, and the projected scale of the application all need to be identified — those factors will determine which LLM will be right for the business.

Specifically for improving e-commerce sales, the LLM could focus on text generation, augmenting listings’ content based on positive and negative sentiment found in online customer reviews, as well as incorporating high volume search terms in the content to boost organic search performance. This makes the content on the product page more compelling and better designed to educate and incentivize the shopper to purchase.

A key factor in the choice of LLM is its ability for fine-tuning and customization. This is what makes AI such a powerful agent for change in the retail market. Fine-tuning is necessary for the LLM to become "expert" in a specific sector, product area or individual product.

This is the critical area which — if done correctly — can have a massively positive impact on sales. The process entails training the chosen LLM by inputting as much relevant existing "best-in-class" content as possible for the LLM to understand what it will be looking for once deployed. For our clients at Luzern, we take clean content examples from Amazon.com that have been Tested by our own data — examples that indicate a high conversion rate, substantial sales, and a large number of positive reviews.

So the baseline is the general model, and the fine-tuning is carried out using a controlled dataset. Once this process is complete, the LLM is embedded and, having learned the sentiments, structures and outputs required, generates new data with the same characteristics. Content on e-commerce sites can then be transformed, catalyzing an increase in sales.

An example of this approach we employed for one of our customers was identifying that its new entry-level product as having huge untapped potential. Research showed that potential customer search terms weren't reflected in how the customer had listed the product description. After identifying and fine-tuning the chosen LLM, we deployed the model and generative AI systematically optimized the product detail page. Some of the results: after just one month, a 71 percent increase in clicks and 64 percent increase in sponsored sales; a 14 percent increase in views and 50 percent increase in organic sales.

Although it's resource-intensive to initially fine-tune the LLM, in terms of ROI it's a no-brainer. However, it does require the right expertise, as it's not difficult to fall foul of the regulatory landscape for AI and cause privacy issues when handling customer data. Being AI-ready isn't as simple as setting up a Chat GPT account. For retail, if implemented properly, AI has the potential to create truly targeted content to extract maximum sales and be a real force for change in our sector.

Cameron Furmidge is the head of insights at Luzern eCommerce, a global e-commerce accelerator solutions provider.

Upfront payments of between $2,500 and $7,000, quietly implemented in January, have started to detract from the entry-level pricing LSI has been promoting, said some solution providers.

Richard Rogers, senior consulting engineer at Swat Systems, a solution provider and MSP in Seattle, said he came onboard as an LPI partner last year, just before the vendor added the start up costs to the pricing model.

"We could have afforded the higher price [of the new pricing model], but a higher price probably would have made us look more at other options," said Rogers.

Swat is currently working with several smaller IT consulting firms in the Seattle area who are looking to resell Swat's LPI's MSP service, because LPI's entry-level pricing has become prohibitively expensive for them, said Rogers.

Ottawa-based LPI " which has been promoting a low-cost entry-level subscription price per customer, per site to remotely monitor customer networks " established new partner pricing in January that requires upfront payments of between $2,500 and $7,000 to get started, said Dan Wensley, vice president of partner development.

But LPI did not publicly announce the change to its pricing model, said Wensley. "We just kind of overlooked it, or the issue got buried," he said.

LPI's January changes require a new partner to pay $2,499 upfront for the vendor's Server Center management console and three one-year customer site licenses instead of paying $60 per month, per customer on a month-to-month basis.

But Wensley said the changes aren't causing sticker shock for partners. Most start out wanting more than one customer site license anyway and understood that there should be an added cost associated with proper training for the LPI product, he said.

"We were bringing on partners for just $60, but we found that wasn't in the best interest of the partners, and it wasn't in the best interest of LPI." he said. "It was such a low commitment that people were just coming on and then going off, and those who did that were just burning up resources."

LPI's ultra-low entry price has been a disruptive factor in the MSP industry. When it lowered its $60 entry price to $15 in February, LPI put aggressive price pressure on rivals such as N-able Technologies, Kaseya, Silverback Technologies, and Cittio. When LPI again lowered its entry price to $5 in May, an all-out price war in the MSP platform market was underway.

One solution provider who is now an MSP and LPI partner said he first contacted LPI about its service early this year and was not told about the upfront costs. When he was ready to sign a contact about three months ago, he was told the vendor had changed its pricing model. This MSP, who requested anonymity, was surprised to hear LPI had raised the cost of entry, but felt the value of the product still justified the higher upfront investment. He purchased a 10-seat license, which LPI lists on its price sheet at $4,999 for one year.

"I guess they have a business to run too," the MSP said.

After the first year, a partner's original batch of site licenses are renewable for a minimum term of one year, and can be paid for in monthly payments at LPI's standard rates, said Wensley. Additional one-year site licenses can be added at any time and paid for on a monthly basis.

Yong Kim is the CEO and cofounder of Wonolo, an on-demand job marketplace that connects workers to jobs posted by businesses across the US.

"What is the sum of the numbers one to 100? How many tennis balls can fit into a yellow school bus? How would you fight a rattlesnake?"

These are some of the more offbeat questions interviewers ask candidates, especially those applying for entry-level jobs. Although it’s clear the goal of inquiring on these brain-teaser questions is to assess the candidates’ ability to think on their feet and problem solve, I find that they are not as useful as some employers believe.

Research shows that these puzzle questions do not help predict the future performance of job candidates. Instead, I believe that they take valuable time away from getting to know the interviewee and whether they are a fit for the position and your company.

So, what are the best questions to ask entry-level interviewees? Some examples might include: "Tell us a challenging situation you faced and how you handled it. Can you elaborate on your educational background and any relevant coursework? What interested you about this particular job and company?”

These are excellent questions to understand the various skill sets or experiences of different candidates. However, it’s still unclear how candidates will perform at the job in real life, no matter how compelling the answers to these questions may be.

So, what should leaders ask instead? What should you as an employer look for during the interview, especially for entry-level positions when prior experience is not a requirement? I believe the answer is punctuality.

Punctuality As The Best Predictor Of Entry-Level Success

Leveraging machine learning techniques, my team examined millions of points of data on entry-level jobs across thousands of companies. This helped us determine punctuality as the most important factor or trait that determines whether a candidate will perform strongly at their entry-level position.

At first, it may sound too fundamental or basic. However, being punctual can reveal several key qualities and characteristics about a person. Here are key traits directly related to punctuality:

Reliability And Trustworthiness

Punctual people tend to be reliable and trustworthy. I've found that they are more likely to keep their commitments and meet deadlines, making them a team member that leaders can rely on. Digging deeper, this reliability trait signals respect for other people’s time and indicates a high level of discipline and self-control. Accordingly, punctual workers are highly regarded in professional settings as their behavior demonstrates professionalism and commitment to responsibilities

Time Management And Organizational Skills

Time management is the end-all-be-all in our fast-paced work environments, but surprisingly, 82% of workers polled said they don't have a time management system. Punctual workers tend to be more conscious of time and have the skill sets to prioritize tasks, plan ahead and focus on their goals. As a result, their punctuality can lead to increased productivity, better efficiency and a stronger overall performance.

The Ability To Perform Under Pressure

Research shows that pressure can be related to underperformance. However, punctual workers are best suited to thrive in high-pressure environments because their detail-oriented nature keeps them focused on the task at hand. For example, if a client requests an urgent project under a tight deadline, many employees will rush into the work without an action plan and just aim to finish it as soon as possible. A punctual worker is likely used to self-imposed deadlines and has the experienced skill set to understand the best approach to divide and conquer these higher-pressure items.

I see being a punctual worker as directly correlated with the skills needed to succeed in just about every entry-level job. On the organizational scale, it also contributes to building a positive professional environment and sets a strong role model for others.

How To Identify If A Candidate Is Punctual

So, if punctuality is a strong indicator of the future performance of a candidate, how should we assess this quality during the interview? Instead of asking general questions about education or prior experiences, look for the following examples:

• How do you handle situations when you know you might be running late for work or an important appointment?

• Can you provide an example of a situation where being punctual had a positive impact on a team?

• How do you plan your routine to ensure you arrive at work or an appointment?

• What steps do you take to ensure you don't forget important deadlines or appointments?

• What do you consider to be an acceptable margin of error for being on time for work or appointments?

Asking about specific instances that demonstrate how candidates think about punctuality can reveal a lot about their overall approach to work, accountability and professionalism. Responses to these questions can also signal the candidate's organizational skills and dedication to staying committed to their projects even under pressure.

Overall, as a business leader or hiring manager, understanding the value of punctuality in an entry-level candidate can help set the foundation for you to grow a strong, reliable and professional workforce.

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