AI-900 Knowledge Mining 1 — Questions and Answers
Question 1: What is the primary purpose of Azure Cognitive Search in the context of knowledge mining?
- To train custom machine learning models on structured data
- To extract insights from unstructured content and make it searchable (Correct answer)
- To manage and deploy AI models to production endpoints
- To generate synthetic data for AI training pipelines
Correct answer: To extract insights from unstructured content and make it searchable
Azure Cognitive Search enables knowledge mining by applying AI enrichment to extract insights from unstructured content such as documents, images, and blobs, making that content searchable.
Question 2: Which component in Azure Cognitive Search defines the sequence of AI enrichment steps applied to documents during indexing?
- Index
- Indexer
- Skillset (Correct answer)
- Data source
Correct answer: Skillset
A skillset is an Azure Cognitive Search object that defines a pipeline of cognitive skills (such as OCR, key phrase extraction, and entity recognition) applied during AI enrichment.
Question 3: In Azure Cognitive Search, what is an indexer responsible for?
- Storing the extracted knowledge in a relational database
- Automating the process of reading data from a source, enriching it, and loading it into an index (Correct answer)
- Defining the schema and field attributes of a search index
- Routing user queries to the appropriate search replica
Correct answer: Automating the process of reading data from a source, enriching it, and loading it into an index
An indexer automates the crawl-enrich-load pipeline by connecting to a data source, optionally running a skillset, and populating the search index with the processed content.
Question 4: Which Azure storage service is most commonly used as a data source for an Azure Cognitive Search indexer when mining documents?
- Azure Table Storage
- Azure Queue Storage
- Azure Blob Storage (Correct answer)
- Azure File Storage
Correct answer: Azure Blob Storage
Azure Blob Storage is the most common data source for Azure Cognitive Search because it can store large volumes of unstructured documents such as PDFs, Word files, and images that benefit from AI enrichment.
Question 5: What does the knowledge store feature in Azure Cognitive Search do?
- Provides a UI for exploring enriched content visually
- Persists AI-enriched content to Azure Storage for downstream analysis (Correct answer)
- Caches search query results to improve performance
- Manages access control for sensitive search indexes
Correct answer: Persists AI-enriched content to Azure Storage for downstream analysis
The knowledge store persists enriched documents produced by a skillset to Azure Blob Storage or Azure Table Storage, enabling downstream analytics and integration with tools like Power BI.
Question 6: Which built-in cognitive skill in Azure Cognitive Search extracts important words and phrases from text?
- Entity Recognition skill
- Key Phrase Extraction skill (Correct answer)
- Language Detection skill
- Sentiment Analysis skill
Correct answer: Key Phrase Extraction skill
The Key Phrase Extraction skill identifies the main talking points in text and outputs them as a list of key phrases, making documents more discoverable.
Question 7: What is the role of a search index in Azure Cognitive Search?
- It stores the original, unenriched source documents
- It defines the credentials used to connect to the data source
- It is a persistent data structure containing searchable content and metadata (Correct answer)
- It specifies which AI model to use for enrichment
Correct answer: It is a persistent data structure containing searchable content and metadata
A search index is the primary searchable data structure in Azure Cognitive Search that contains the enriched, indexed fields that users query against.
AI-900 Knowledge Mining 1
What is the primary purpose of Azure Cognitive Search in the context of knowledge mining?