AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Using AI to fully automate all HR decisions without human involvement

  Why it's wrong here

  Fully automating consequential HR decisions with no human oversight is not the intended use of AI in HR; it raises legal and ethical issues such as disparate-impact bias and lack of explainability. Modern practice employs human-in-the-loop design, where machine learning provides decision support (e.g., ranking candidates) while a human makes the final call, especially for protected-class implications. Therefore, while AI can analyze data, it should augment, not replace, HR judgment.

• ✓

  CV screening, employee sentiment, attrition prediction, skills analysis, and learning recommendations

  Why this is correct

  AI in HR typically applies machine learning and natural language processing across the talent lifecycle, as seen in CV screening (parsing resumes for skills and experience), employee sentiment analysis (classifying survey feedback), attrition prediction (identifying churn risk from historical patterns), skills analysis (inferring competencies from job histories), and learning recommendations (personalized course suggestions). This combination highlights how AI's core abilities including text analysis, forecasting, and recommendation engines support human-resources functions. However, such uses require responsible AI practices to mitigate bias in hiring and performance evaluations.

• ✗

  Managing employee payroll and benefits calculations using traditional database systems

  Why it's wrong here

  Managing payroll and benefits calculations with traditional database systems is a deterministic ERP function, not AI. Payroll uses fixed rules and arithmetic to compute wages or tax deductions, while AI in HR is geared toward machine learning and NLP applied to unstructured data like text, and toward predictive analytics such as churn or sentiment. Traditional DBs store and query data; they do not learn patterns or make probabilistic recommendations, so this option falls outside AI for HR.

• ✗

  Ensuring HR documents comply with employment law using rule-based systems

  Why it's wrong here

  Ensuring compliance of HR documents with employment law via rule-based systems relies on explicit if-then logic and manual policy encoding, which is closer to conventional legal or automation software. AI in HR, by contrast, uses machine learning to derive insights and predictions from workforce data, such as detecting patterns in attrition sentiment or skill gaps. Rule-based compliance does not involve model training or adaptive inference, so it represents a traditional software approach rather than an AI workload.

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