CCNA Implement knowledge mining and information extraction solutions Questions

Option D is correct because Azure AI Search can index documents and be used as a data source for a question-answering system, and Azure AI Bot Service can host the chatbot. Option A is wrong because Language Service's QnA Maker (now Custom Question Answering) can use a knowledge base but not directly index documents; it requires a search index for large datasets. Option B is wrong because Document Intelligence extracts data but does not answer questions.

Option C is wrong because Computer Vision does not handle text queries.

Option C is correct because you can use the Text Translation skill to translate content to English during indexing, and then index only the translated text. Option A would not translate. Option B only detects language.

Option D uses two skills unnecessarily.

A, B, and D are correct. A: The Web API is the endpoint. B: JSON input/output format is required.

D: Field mappings define inputs and outputs in the skillset. C is not required; E is optional.

Options A and D are correct. A: Providing managed identity allows the indexer to authenticate to the Azure Function hosting the custom skill. D: Configuring a retry policy ensures transient failures are retried.

Option B is wrong because CORS is not needed for indexer-to-function calls (they are server-to-server). Option C is wrong because the indexer does not call the API directly; it calls the custom skill endpoint. Option E is wrong because the indexer already has its own timeout settings.

Options A and D are correct. Scoring profiles allow boosting by field values or freshness. Semantic ranking re-ranks results to improve relevance.

Option B is wrong because synonym maps improve recall, not relevance. Option C is wrong because filters restrict results but do not improve relevance ranking. Option E is wrong because indexers are for data ingestion, not relevance.

Option A is correct because enabling incremental enrichment caches intermediate results and recovers from failures without re-processing unchanged documents, reducing cost. Option B is incorrect because increasing the number of replicas improves query performance, not indexing. Option C is incorrect because disabling field mappings would break the pipeline.

Option D is incorrect because using a free tier is not feasible for large volumes.

Option B is correct because by assigning a higher weight to the 'tags' field, search results that match tags will rank higher. Option A changes analyzers but doesn't address field weighting. Option C only affects the 'content' field.

Option D is about scoring profiles but not specifically about field weighting.

Options A, C, and D are correct. A skillset with key phrase extraction and language detection skills (A), a data source connecting to the documents (C), and a search index to store the enriched data (D). Option B is wrong because the indexer is required, but the index is needed too.

Option E is wrong because custom skills are not required.

Option A is correct because semantic search improves relevance by understanding natural language queries and providing answer-style results. Synonyms (B) help with query expansion but not natural language understanding. Knowledge mining (C) is a broader process.

Cognitive skills (D) are for enrichment, not query-time interpretation.

Option B is correct because Azure AI Health Insights (formerly Text Analytics for Health) is specifically designed to extract medical entities from clinical text. Option A is incorrect because Azure AI Language provides general entity extraction but not healthcare-specific. Option C is incorrect because Azure AI Document Intelligence is for document extraction, not clinical notes.

Option D is incorrect because Azure AI Search is the indexing service, not the extraction service.

The indexer allows up to 2 failed items per batch (maxFailedItemsPerBatch=2). Since 3 > 2, the entire batch fails. The indexer continues with the next batch if maxFailedItems (overall) is not exceeded.

In this case, overall maxFailedItems is 5, so the indexer continues overall.

Option B is correct because the key phrase extraction model does not perform sentiment analysis; it extracts phrases based on frequency and relevance, ignoring negation. Option A is wrong because the language model is accurate for most languages. Option C is wrong because prebuilt models are generally robust.

Option D is wrong because the service does not have a parameter to exclude negative phrases.

Option C is correct: the output field mapping in the indexer is missing or incorrect. Option A is incorrect because the skill ran successfully. Option B is incorrect because the sentiment skill does not require a specific data type.

Option D is incorrect because indexer execution is successful.

Option B is correct because the push API allows you to send updates directly to the index, providing near-real-time indexing. Option A is wrong because indexer-based scheduled indexing has a minimum interval of 5 minutes, and changes may not be picked up immediately. Option C is wrong because incremental enrichment is for AI enrichment, not for data updates.

Option D is wrong because semantic search does not affect update frequency.

Option A is correct. Using Azure AI Document Intelligence to chunk documents into meaningful sections preserves context, and generating embeddings with Azure OpenAI allows vector search for RAG. Option B is incorrect because splitting by page may break tables and footnotes.

Option C is incorrect because keyword search alone does not support semantic understanding. Option D is incorrect because Azure AI Language does not handle PDF extraction or vector generation.

Semantic search in Azure AI Search uses deep learning models to understand the intent behind queries and improve relevance of results. Enabling semantic search on the index addresses the natural language relevance issue without requiring application changes. Option A is wrong because creating a new index with blob indexers does not change relevance.

Option B is wrong because adding a custom analyzer helps with tokenization, not semantic understanding. Option D is wrong because increasing the number of replicas improves throughput, not relevance.

Option D is correct because a custom skill can evaluate document size and conditionally call built-in skills. Option A is wrong because skills run on all documents regardless of size. Option B is wrong because indexer parameters cannot conditionally skip skills based on document content.

Option C is wrong because skillset execution order cannot be changed by document size.