Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is anomaly detection in the context of AI workloads?
⚠ Common exam trap
A common mix-up: 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.
Answer choices
Why each option matters
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.
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
Classifying images into 'normal' and 'abnormal' categories is a supervised image classification task, where a model is trained on labeled examples to assign discrete class labels. Anomaly detection, in contrast, typically uses unsupervised techniques to model the distribution of normal data and flag any observation that falls outside that learned distribution. While image anomaly detection exists, the core AI-900 definition emphasizes statistical deviation in data patterns rather than predefined categorical labeling.
- ✓
Identifying data points that deviate significantly from expected patterns
Why this is correct
Anomaly detection identifies data points, events, or patterns that deviate significantly from an established baseline or expected behavior. It is commonly implemented with statistical methods (e.g., z-score, Grubbs' test) or machine learning models (e.g., isolation forests, one-class SVM) that learn what 'normal' looks like and then flag outliers. This aligns with Azure Anomaly Detector, which analyzes time-series data to detect spikes, dips, or unexpected pattern changes.
- ✗
Detecting grammatical errors in text
Why it's wrong here
Detecting grammatical errors in text is an NLP task that uses part-of-speech tagging, syntactic parsing, and language models to identify rule violations or awkward phrasing. Anomaly detection operates on numerical or categorical data patterns, not on linguistic rules or semantic correctness. Although text anomaly detection could flag unusual document themes, it does not identify grammar mistakes, which require language-specific understanding.
- ✗
Finding duplicate records in a database
Why it's wrong here
Finding duplicate records in a database is a data quality management task that compares records using key fields or similarity metrics such as fuzzy matching to identify identical or near-identical entries. Anomaly detection, by contrast, focuses on observations that are statistically rare or unexpected relative to the majority of data, such as a sudden drop in server throughput. Deduplication preserves data integrity, whereas anomaly detection flags exceptional events for monitoring and investigation.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
Key term
Clustering
Clustering is a technique where multiple servers work together as a single system to keep applications running even if one server fails.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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