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AI-900 Practice Question: 'causal inference' and how does it differ from…
This AI-900 practice question tests your understanding of 'causal inference' and how does it differ from…. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'causal inference' and how does it differ from correlation-based machine learning?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Causal inference uses larger training datasets; correlation-based ML uses smaller ones
Dataset size is unrelated — the distinction is about inferring whether a statistical relationship represents actual causation.
B
Best answer
Causal inference determines whether X actually causes Y; ML finds correlations that predict outcomes
Correlation ≠ causation — causal inference uses explicit causal reasoning, counterfactuals, and interventions rather than just predictive patterns.
C
Distractor review
ML models always establish causal relationships; causal inference is needed only when data quality is poor
Standard ML makes no causal claims — finding correlation is not the same as establishing causation, regardless of data quality.
D
Distractor review
Causal inference is exclusively used in medical research; ML is used in business applications
Both are domain-agnostic — causal inference is increasingly important in AI fairness, policy, and economic applications.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?
Question 2
A developer is building a customer support chatbot using Azure OpenAI. The chatbot should never reveal its system instructions or internal configuration. The developer wants to add a rule at the beginning of the conversation to prevent prompt injection attacks. Which technique should they use?
Question 3
A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?
Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
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FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Causal inference determines whether X actually causes Y; ML finds correlations that predict outcomes — Standard ML finds correlations — inputs that statistically predict outputs. Causal inference determines whether X actually causes Y, not just correlates with it. Classic example: ice cream sales correlate with drowning rates (both driven by summer heat), but ice cream doesn't cause drowning. ML models can be misled by spurious correlations; causal models explicitly reason about interventions and counterfactuals. This distinction matters for fair AI — a model using zip code as a proxy for race finds correlation, not cause.
What should I do if I get this AI-900 question wrong?
Identify which AI-900 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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