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Question 450
Image labels were generated by different teams with inconsistent meanings. Which step should precede model training?
- AutoML model building
- Fine-tuning a foundation model
- Data labeling and preparation
- Foundation model prompting
Explanation
Correct Answer
C:Label harmonization is part of preparation. Data preparation ensures examples and targets are suitable before training or evaluation.
Incorrect Answers
A:AutoML automates parts of training a task-specific model from prepared business data. This does not directly meet the case requirement to ensure training targets use consistent definitions.
B:Fine-tuning adapts a pretrained model to a specialized task through additional learning. This does not directly meet the case requirement to ensure training targets use consistent definitions.
D:Prompting directs a pretrained generative model without a new training job. This does not directly meet the case requirement to ensure training targets use consistent definitions.
Question 451
A help app must find the most relevant paragraph in a large policy index before answering. Which component performs this?
- Grounded model generation
- RAG Engine or RAG API
- Embedding generation
- Agent Search retrieval
Explanation
Correct Answer
D:Agent Search can serve as an enterprise retrieval engine. Agent Search retrieves relevant passages and results from configured indexed sources.
Incorrect Answers
A:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to retrieve relevant indexed documents.
B:RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data. This does not directly meet the case requirement to retrieve relevant indexed documents.
C:Embedding models encode passages and queries as vectors for similarity search. This does not directly meet the case requirement to retrieve relevant indexed documents.
Question 452
A developer wants a customizable retrieval pipeline instead of only a prebuilt search widget. Which platform capability fits?
- Embedding generation
- Grounded model generation
- RAG Engine or RAG API
- Retrieval evaluation
Explanation
Correct Answer
C:RAG APIs offer retrieval components for tailored applications. RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data.
Incorrect Answers
A:Embedding models encode passages and queries as vectors for similarity search. This does not directly meet the case requirement to build a custom RAG retrieval workflow.
B:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to build a custom RAG retrieval workflow.
D:Retrieval evaluation measures relevance and whether evidence supports generated claims. This does not directly meet the case requirement to build a custom RAG retrieval workflow.
Question 453
A search engine compares query meaning to document vectors. Which model step creates the representation?
- RAG Engine or RAG API
- Embedding generation
- Agent Search retrieval
- Grounded model generation
Explanation
Correct Answer
B:Embeddings transform content into comparable numerical forms. Embedding models encode passages and queries as vectors for similarity search.
Incorrect Answers
A:RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data. This does not directly meet the case requirement to encode semantic meaning for similarity search.
C:Agent Search retrieves relevant passages and results from configured indexed sources. This does not directly meet the case requirement to encode semantic meaning for similarity search.
D:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to encode semantic meaning for similarity search.
Question 454
After relevant sources are retrieved, an assistant must combine them into a readable reply. Which operation is next?
- Grounded model generation
- RAG Engine or RAG API
- Agent Search retrieval
- Source access enforcement
Explanation
Correct Answer
A:Grounded generation composes a response from supplied evidence. A model uses retrieved context to compose a supported answer.
Incorrect Answers
B:RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data. This does not directly meet the case requirement to write an answer using retrieved facts.
C:Agent Search retrieves relevant passages and results from configured indexed sources. This does not directly meet the case requirement to write an answer using retrieved facts.
D:Access controls ensure the retrieval path does not expose restricted content. This does not directly meet the case requirement to write an answer using retrieved facts.
Question 455
A restricted HR file appears in an AI search answer for an intern. Which control is missing?
- Source access enforcement
- Retrieval evaluation
- Grounded model generation
- RAG Engine or RAG API
Explanation
Correct Answer
A:Authorization must be enforced when retrieving content. Access controls ensure the retrieval path does not expose restricted content.
Incorrect Answers
B:Retrieval evaluation measures relevance and whether evidence supports generated claims. This does not directly meet the case requirement to prevent disclosure through RAG retrieval.
C:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to prevent disclosure through RAG retrieval.
D:RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data. This does not directly meet the case requirement to prevent disclosure through RAG retrieval.
Question 456
A RAG system cites irrelevant sources for customer questions. Which test should be implemented?
- Grounded model generation
- Embedding generation
- Source access enforcement
- Retrieval evaluation
Explanation
Correct Answer
D:Retrieval quality needs measured relevance and answer support. Retrieval evaluation measures relevance and whether evidence supports generated claims.
Incorrect Answers
A:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to evaluate selected passages against correct sources.
B:Embedding models encode passages and queries as vectors for similarity search. This does not directly meet the case requirement to evaluate selected passages against correct sources.
C:Access controls ensure the retrieval path does not expose restricted content. This does not directly meet the case requirement to evaluate selected passages against correct sources.
Question 457
An organization wants Google-quality semantic search over its governed manuals. Which managed retrieval offering is appropriate?
- Agent Search retrieval
- Grounded model generation
- Retrieval evaluation
- Embedding generation
Explanation
Correct Answer
A:Agent Search provides configured semantic retrieval. Agent Search retrieves relevant passages and results from configured indexed sources.
Incorrect Answers
B:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to find internal manual passages by meaning.
C:Retrieval evaluation measures relevance and whether evidence supports generated claims. This does not directly meet the case requirement to find internal manual passages by meaning.
D:Embedding models encode passages and queries as vectors for similarity search. This does not directly meet the case requirement to find internal manual passages by meaning.
Question 458
A custom agent needs to retrieve small chunks from private documents through managed RAG components. Which capability should be examined?
- Grounded model generation
- Retrieval evaluation
- Source access enforcement
- RAG Engine or RAG API
Explanation
Correct Answer
D:RAG Engine and APIs support application-specific retrieval. RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data.
Incorrect Answers
A:A model uses retrieved context to compose a supported answer. This does not directly meet the case requirement to connect a model to indexed enterprise context.
B:Retrieval evaluation measures relevance and whether evidence supports generated claims. This does not directly meet the case requirement to connect a model to indexed enterprise context.
C:Access controls ensure the retrieval path does not expose restricted content. This does not directly meet the case requirement to connect a model to indexed enterprise context.
Question 459
The retrieval stage returns correct paragraphs, but the app has no synthesis step. What capability is missing?
- Embedding generation
- Grounded model generation
- Source access enforcement
- RAG Engine or RAG API
Explanation
Correct Answer
B:Grounded model generation converts evidence into an answer. A model uses retrieved context to compose a supported answer.
Incorrect Answers
A:Embedding models encode passages and queries as vectors for similarity search. This does not directly meet the case requirement to compose a useful response based on passages.
C:Access controls ensure the retrieval path does not expose restricted content. This does not directly meet the case requirement to compose a useful response based on passages.
D:RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data. This does not directly meet the case requirement to compose a useful response based on passages.
Question 460
A search pilot has low factual citation accuracy despite good model fluency. What should be measured?
- Agent Search retrieval
- Source access enforcement
- RAG Engine or RAG API
- Retrieval evaluation
Explanation
Correct Answer
D:Relevance and grounding evaluations test the retrieval-to-answer chain. Retrieval evaluation measures relevance and whether evidence supports generated claims.
Incorrect Answers
A:Agent Search retrieves relevant passages and results from configured indexed sources. This does not directly meet the case requirement to measure evidence quality and citation support.
B:Access controls ensure the retrieval path does not expose restricted content. This does not directly meet the case requirement to measure evidence quality and citation support.
C:RAG services provide infrastructure for chunking, retrieving and grounding with application-specific data. This does not directly meet the case requirement to measure evidence quality and citation support.
Generative AI Leader Practice Tests
