AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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 anomaly detection in the context of AI workloads?
Classifying images into categories of 'normal' and 'abnormal'
Why wrong: Image classification uses predefined categories — anomaly detection finds data points that deviate from expected patterns across many data types.
B
Identifying data points that deviate significantly from expected patterns
Anomaly detection flags unusual values or patterns in data — used for fraud detection, equipment monitoring, and security.
C
Detecting grammatical errors in text
Why wrong: Grammar checking is an NLP task — anomaly detection identifies statistical outliers in data patterns.
D
Finding duplicate records in a database
Why wrong: Duplicate detection is data quality management — anomaly detection identifies statistically unusual patterns in time series or other data.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Identifying data points that deviate significantly from expected patterns
Anomaly detection is an AI technique that identifies data points, events, or observations that deviate significantly from the majority of the data or from expected patterns. In AI workloads, this is typically implemented using statistical methods, clustering algorithms (like k-means), or neural networks (e.g., autoencoders) to flag outliers for further investigation. Option B correctly captures this core definition, as anomaly detection is fundamentally about finding deviations, not about classification, grammar, or duplication.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Classifying images into categories of 'normal' and 'abnormal'
Why it's wrong here
Image classification uses predefined categories — anomaly detection finds data points that deviate from expected patterns across many data types.
✓
Identifying data points that deviate significantly from expected patterns
Why this is correct
Anomaly detection flags unusual values or patterns in data — used for fraud detection, equipment monitoring, and security.
Related concept
Read the scenario before looking for a memorised answer.
✗
Detecting grammatical errors in text
Why it's wrong here
Grammar checking is an NLP task — anomaly detection identifies statistical outliers in data patterns.
✗
Finding duplicate records in a database
Why it's wrong here
Duplicate detection is data quality management — anomaly detection identifies statistically unusual patterns in time series or other data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse anomaly detection with classification (Option A) because both can output 'normal' vs. 'abnormal' labels, but anomaly detection is unsupervised or semi-supervised and does not require pre-labeled training data for all anomaly types, whereas classification requires a balanced labeled dataset.
Detailed technical explanation
How to think about this question
Under the hood, anomaly detection often relies on density-based methods like Local Outlier Factor (LOF) or isolation forests, which isolate anomalies by randomly partitioning data; anomalies require fewer splits to isolate because they are few and different. In real-world scenarios, such as detecting fraudulent credit card transactions, anomaly detection models must handle concept drift—where normal behavior changes over time—requiring continuous retraining or adaptive thresholds. A subtle behavior is that anomaly detection can produce false positives when rare but legitimate events occur, so tuning the contamination parameter (e.g., in scikit-learn's IsolationForest) is critical.
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.
TExam Day Tips
→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.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Identifying data points that deviate significantly from expected patterns — Anomaly detection is an AI technique that identifies data points, events, or observations that deviate significantly from the majority of the data or from expected patterns. In AI workloads, this is typically implemented using statistical methods, clustering algorithms (like k-means), or neural networks (e.g., autoencoders) to flag outliers for further investigation. Option B correctly captures this core definition, as anomaly detection is fundamentally about finding deviations, not about classification, grammar, or duplication.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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