- A
A legal analysis technique for discovering hidden clauses in contracts
Why wrong: Contract analysis is legal AI — LSA is an NLP technique for finding latent topics in document collections.
- B
An early matrix factorisation method for finding latent semantic relationships — predecessor to neural embeddings
LSA uses SVD to discover semantic relatedness — the conceptual predecessor to modern transformer-based semantic embeddings.
- C
A technique for analysing the structure of sentences to detect grammatical errors
Why wrong: Grammar detection is syntax analysis — LSA analyses document-term relationships to find latent semantic topics.
- D
Latent Semantic Analysis is the same as Large Language Model analysis
Why wrong: LSA and LLMs are entirely different — LSA is a classic matrix factorisation technique; LLMs use deep neural networks.
Quick Answer
The correct choice is latent semantic analysis, an early matrix factorization method that serves as a direct predecessor to modern neural embeddings. LSA uses singular value decomposition (SVD) to reduce the dimensionality of a term-document matrix, uncovering latent semantic relationships between words and documents based on co-occurrence patterns. This contrasts with modern NLP embeddings like Word2Vec or GloVe, which learn low-dimensional vector representations through deeper non-linear transformations. On the AI-900 exam, this distinction tests your understanding of how foundational techniques evolved into today’s neural approaches—a common trap is confusing LSA’s linear SVD reduction with the non-linear, context-aware learning of embeddings. Remember the memory tip: LSA is “linear SVD, old-school; embeddings are neural, context-cool.”
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. 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 'latent semantic analysis' (LSA) and how does it relate to modern NLP embeddings?
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
An early matrix factorisation method for finding latent semantic relationships — predecessor to neural embeddings
Option B is correct because Latent Semantic Analysis (LSA) is an early matrix factorization technique that uses singular value decomposition (SVD) to reduce the dimensionality of a term-document matrix, revealing latent semantic relationships between words and documents. This approach is a direct predecessor to modern neural embeddings (e.g., Word2Vec, GloVe), which also learn low-dimensional vector representations of words based on co-occurrence patterns, but with deeper non-linear transformations.
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.
- ✗
A legal analysis technique for discovering hidden clauses in contracts
Why it's wrong here
Contract analysis is legal AI — LSA is an NLP technique for finding latent topics in document collections.
- ✓
An early matrix factorisation method for finding latent semantic relationships — predecessor to neural embeddings
Why this is correct
LSA uses SVD to discover semantic relatedness — the conceptual predecessor to modern transformer-based semantic embeddings.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A technique for analysing the structure of sentences to detect grammatical errors
Why it's wrong here
Grammar detection is syntax analysis — LSA analyses document-term relationships to find latent semantic topics.
- ✗
Latent Semantic Analysis is the same as Large Language Model analysis
Why it's wrong here
LSA and LLMs are entirely different — LSA is a classic matrix factorisation technique; LLMs use deep neural networks.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'latent' with 'legal' or assume LSA is a modern deep learning technique, when in fact it is a classical linear algebra method that predates neural embeddings and is not used in contemporary LLMs.
Detailed technical explanation
How to think about this question
Under the hood, LSA applies SVD to a term-document matrix, decomposing it into three matrices (U, Σ, V^T) and then truncating to the top k singular values to produce a low-rank approximation. This captures synonymy and polysemy by grouping terms that appear in similar contexts, but it lacks the non-linear, contextualized representations of modern embeddings like BERT, which use attention mechanisms to dynamically adjust word meaning based on surrounding words. In real-world Azure AI workloads, LSA might be used in legacy document clustering or topic modeling, but for production NLP tasks, Azure Cognitive Services leverages neural embeddings for more accurate semantic search and sentiment analysis.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: An early matrix factorisation method for finding latent semantic relationships — predecessor to neural embeddings — Option B is correct because Latent Semantic Analysis (LSA) is an early matrix factorization technique that uses singular value decomposition (SVD) to reduce the dimensionality of a term-document matrix, revealing latent semantic relationships between words and documents. This approach is a direct predecessor to modern neural embeddings (e.g., Word2Vec, GloVe), which also learn low-dimensional vector representations of words based on co-occurrence patterns, but with deeper non-linear transformations.
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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Last reviewed: Jun 11, 2026
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