Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A botanist uses Azure Automated Machine Learning to train a model that classifies iris flowers into three species: setosa, versicolor, and virginica. The dataset contains exactly 50 examples of each species, making it perfectly balanced. The botanist wants the primary metric to give equal importance to the classification performance of each species, regardless of their frequency. Which primary metric should the botanist select in Azure AutoML?
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
Accuracy
Incorrect because accuracy is the overall proportion of correct predictions; while it is a reasonable metric for balanced data, it does not explicitly treat each class's performance equally—it treats each instance equally.
B
Distractor review
Weighted F1
Incorrect because weighted F1 computes the average of per-class F1 scores weighted by the number of true instances per class; although this equals macro F1 when classes are perfectly balanced, the principle of equal importance per class is better captured by macro F1, which is designed for that purpose.
C
Best answer
Macro F1
Correct because macro F1 averages the F1 scores of all classes without weighting by class size, thereby giving equal importance to the classification performance of each species.
D
Distractor review
Micro F1
Incorrect because micro F1 aggregates global counts of true positives, false positives, and false negatives across all classes; it does not give equal weight to each class and is influenced by larger classes, even in a balanced dataset.
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.
Related practice questions
Related AI-900 practice-question pages
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?
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: Macro F1 — Macro F1 calculates the F1 score for each class independently and then averages them, giving equal weight to each class's performance. In a balanced dataset, other metrics may yield similar numbers, but the specification of 'equal importance to each species' explicitly points to macro F1 as the correct metric. Accuracy treats each prediction equally, not each class, and weighted/micro F1 are influenced by class frequencies.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
Discussion
Loading comments…
Sign in to join the discussion.