Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
What is 'latent semantic analysis' (LSA) and how does it relate to modern NLP embeddings?
⚠ Common exam trap
Many exam-takers 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.
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
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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A legal analysis technique for discovering hidden clauses in contracts
Why it's wrong here
The suggestion that LSA is a legal-analysis technique for contract clauses is incorrect; legal AI uses specialised tools like named entity recognition, clause classification, and annotation-based contract review systems. LSA is a generic, unsupervised NLP technique for discovering latent topics in any document collection, with no inherent legal logic or framing, so it is not specifically suited to parsing hidden clauses in contracts.
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An early matrix factorisation method for finding latent semantic relationships — predecessor to neural embeddings
Why this is correct
Latent Semantic Analysis (LSA) is correctly described as an early matrix factorisation technique: it applies singular value decomposition (SVD) to a term-document matrix, producing a low-rank latent space that captures semantic relatedness between terms and documents. This yields dense, distributional word vectors that anticipate modern neural embeddings, though LSA's representations are linear and static rather than context-sensitive, which is why it is viewed as a conceptual predecessor to transformer-based embeddings.
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A technique for analysing the structure of sentences to detect grammatical errors
Why it's wrong here
The description 'structural analysis for detecting grammatical errors' refers to syntactic parsing and grammar checking, which rely on part-of-speech tagging and dependency trees. In contrast, LSA does not parse sentence structure; it operates on an unordered bag-of-words matrix, using co-occurrence statistics to derive latent topic dimensions, and thus cannot detect grammatical mistakes.
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Latent Semantic Analysis is the same as Large Language Model analysis
Why it's wrong here
LSA is not equivalent to Large Language Model analysis: LSA is a classic linear-algebraic method that performs SVD on a static term-document matrix, producing fixed word embeddings, whereas LLMs such as GPT are deep neural networks with billions of parameters that generate contextualised token representations through attention mechanisms. They differ fundamentally in model architecture, training data scale, and ability to handle word order, semantics, and generation.
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