Describe Artificial Intelligence workloads and considerations for Microsoft Azure AI Fundamentals (AI-900)

Sample Practice Questions

1. Question 1

   You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?
   1. A. Set Validation type to Auto.
   2. B. Enable Explain best model.
   3. C. Set Primary metric to accuracy.
   4. D. Set Max concurrent iterations to 0.
   Explanation

   The correct answer is: B. Enable Explain best model..

   Enabling Explain best model in the automated machine learning UI of Azure Machine Learning produces feature importance and other interpretability artifacts for the winning model, which is exactly what the transparency principle calls for: stakeholders should be able to see how the model reaches its decisions and which inputs drive them. Setting Validation type to Auto controls how the training and validation splits are chosen, which influences accuracy estimation rather than explainability. Choosing accuracy as the primary metric tells AutoML which scoring measure to optimize and may even hide bias issues, but it does not surface the model's reasoning. Setting Max concurrent iterations to 0 simply caps how many child runs execute in parallel, a performance and capacity setting that has no bearing on whether the resulting model is interpretable to users or reviewers.

2. Question 2

   HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:
   Explanation

   Reliability and safety: AI systems need to be reliable and safe in order to be trusted. It is important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process. An AI system's performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactively and proactively measure the model's performance and retrain it, as necessary, to modernize it. Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

3. Question 3

   DRAG DROP - Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:
   Explanation

   Box 3: Natural language processing Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

4. Question 4

   You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments. This is an example of which Microsoft guiding principle for responsible AI?
   1. A. fairness
   2. B. inclusiveness
   3. C. reliability and safety
   4. D. accountability
   Explanation

   The correct answer is: B. inclusiveness.

   Designing an AI system so that people with hearing, visual, or other impairments can use it is the textbook example of the inclusiveness principle, which requires that AI empower everyone regardless of ability, language, or background. In practice this means supporting captions, screen readers, alternative input methods, and diverse linguistic and cultural contexts during design and testing. Fairness is related but distinct: it focuses on equitable outcomes across groups once people are using the system, not on whether the system is accessible to them in the first place. Reliability and safety addresses how consistently and safely the system behaves under varied conditions, which is a different concern from accessibility. Accountability is about humans owning the governance and outcomes of the system, again separate from the accessibility goal that defines inclusiveness.

5. Question 5

   DRAG DROP - Match the Microsoft guiding principles for responsible AI to the appropriate descriptions. To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:
   Explanation

   Box 1: Reliability and safety - To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Box 2: Accountability - The people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems. Box 3: Privacy and security - As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

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