The answer is Azure AI Anomaly Detector. This service is purpose-built for anomaly detection in time series data, using machine learning algorithms to automatically identify spikes, dips, or cyclical pattern changes without requiring any custom model training or labeled data. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to match specific Azure AI services to their core functions, often appearing alongside traps like Azure Machine Learning or Azure Cognitive Services—both of which are broader platforms, not dedicated anomaly detection tools. A reliable memory tip is to think of the word "Detector" as the key: if the task involves spotting outliers in sequential data over time, the Anomaly Detector is the only pre-built, single-purpose service designed for that exact job.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Which Azure AI service is purpose-built for detecting anomalies in time series data?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Anomaly Detector
Azure AI Anomaly Detector is a dedicated, pre-built service specifically designed to identify anomalies in time series data without requiring custom model training. It uses machine learning algorithms to automatically detect spikes, dips, or pattern changes in sequential data, making it the correct choice for this purpose.
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.
✗
Azure Machine Learning with custom anomaly detection models
Why it's wrong here
Custom models require ML expertise — Azure AI Anomaly Detector is purpose-built with no ML expertise required.
✓
Azure AI Anomaly Detector
Why this is correct
Anomaly Detector is a purpose-built managed service for detecting anomalies in time series data without custom model training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure AI Language sentiment analysis
Why it's wrong here
Sentiment analysis evaluates text emotional tone — Anomaly Detector finds statistical outliers in time series data.
✗
Azure AI Vision spatial analysis
Why it's wrong here
Spatial analysis tracks people in video — Anomaly Detector is for time series data anomaly detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between a purpose-built service (Anomaly Detector) and a general-purpose platform (Azure Machine Learning), leading candidates to incorrectly choose the latter because they assume custom models are always required for anomaly detection.
Detailed technical explanation
How to think about this question
Azure AI Anomaly Detector employs multiple algorithms, including SR-CNN (Spectral Residual with Convolutional Neural Networks) for seasonal data and multivariate anomaly detection using graph-based models. It automatically handles seasonality, trend decomposition, and data normalization, returning anomaly scores and expected values for each data point. A real-world scenario is monitoring server CPU usage over time to detect unexpected spikes that could indicate a cyberattack or hardware failure.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Azure AI Anomaly Detector — Azure AI Anomaly Detector is a dedicated, pre-built service specifically designed to identify anomalies in time series data without requiring custom model training. It uses machine learning algorithms to automatically detect spikes, dips, or pattern changes in sequential data, making it the correct choice for this purpose.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is anomaly detection in the context of AI workloads?
medium
A.Classifying images into categories of 'normal' and 'abnormal'
✓ B.Identifying data points that deviate significantly from expected patterns
C.Detecting grammatical errors in text
D.Finding duplicate records in a database
Why B: 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.
Variation 2. Which of the following is an example of 'anomaly detection' as an AI workload?
easy
A.Translating customer support emails from Spanish to English
✓ B.Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns
C.Generating product descriptions from a list of specifications
D.Classifying customer reviews as positive or negative
Why B: Anomaly detection identifies data points that deviate significantly from the norm. In this case, fraudulent credit card transactions are detected because they do not match the customer's typical spending patterns, which is a classic use case for anomaly detection in AI workloads.
Last reviewed: Jun 30, 2026
Question Discussion
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