AI-200 Data Management Services And Vector Search — All Questions With Answers

Question 1hardmultiple choice

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You are optimizing an Azure AI Search vector index with millions of documents. Users report high latency during vector queries. You decide to implement vector compression to reduce memory footprint and improve search throughput. Which compression method is natively supported in Azure AI Search?

Question 2mediummultiple choice

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You are developing a retrieval-augmented generation (RAG) solution using Azure AI Search. You need to configure a vector index to store 1536-dimensional embeddings generated by text-embedding-ada-002. Which parameter must you configure in the vector profile's algorithm configuration to use HNSW as the underlying approximate nearest neighbor algorithm?

Question 3mediummulti select

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You are configuring a vector search profile in Azure AI Search. Which TWO parameters are required when defining an algorithm configuration using the HNSW algorithm? (Choose two)

Question 4hardmultiple choice

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You are managing an Azure AI Search index that uses integrated vectorization with Azure OpenAI. When a user sends a query string, you want Azure AI Search to automatically vectorize the query text before performing the vector search. Which feature must you configure in the index schema?

Question 5hardmultiple choice

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You are designing a multi-tenant AI application using Azure Cosmos DB for NoSQL. Each tenant has their own set of embeddings, and you need to filter vector search results strictly by tenantId while maintaining high query performance. How should you structure your vector index definition?

Question 6easymultiple choice

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When querying an Azure AI Search vector index using the REST API or SDK, what parameter specifies the number of nearest neighbors to retrieve?

Question 7hardmultiple choice

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You are building a multi-modal AI application that searches across both product images and product descriptions. You store your data in Azure AI Search. How should you define your index schema to support searching with both image embeddings and text embeddings?

Question 8hardmulti select

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You are troubleshooting a slow vector query performance issue in Azure Cosmos DB for NoSQL. Which THREE actions can you take to improve vector search execution times? (Choose three)

Question 9hardmulti select

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You are planning the disaster recovery and high availability strategy for an Azure AI Search service storing critical vector indexes for an AI assistant. Which THREE practices should you implement? (Choose three)

Question 10mediummulti select

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When designing an AI architecture that uses Azure AI Search, which TWO methods can be used to ingest documents and their vector embeddings into the search index? (Choose two)

Question 11hardmultiple choice

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You are managing an Azure AI Search index with millions of high-dimensional vectors. Users experience intermittent timeout errors during peak query hours. Upon investigation, you find that the search service CPU utilization is at 100%. What is the most effective architectural mitigation?

Question 12hardmultiple choice

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You are migrating a large vector dataset into an Azure AI Search index. To maximize ingestion speed and throughput, you temporarily adjust index settings. Which index configuration property should you modify during bulk ingestion?

Question 13mediummulti select

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When configuring vector search in Azure Cosmos DB for NoSQL, which TWO data types are valid for storing vector embeddings within the vector embedding policy? (Choose two)

Question 14mediummulti select

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When configuring an Azure AI Search index, which TWO data types can be used for metadata fields that require exact filtering (e.g., category eq 'electronics')? (Choose two)

Question 15mediummulti select

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When configuring an Azure AI Search index, which TWO field attributes are required if a field is going to be used for full-text keyword search and filtering? (Choose two)

Question 16easymultiple choice

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Which Azure portal blade allows you to view and manage access keys and connection strings for an Azure AI Search service?

Question 17mediummulti select

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When configuring vector search in Azure AI Search, which TWO compression options or algorithm settings can be defined to optimize resource usage? (Choose two)

Question 18mediummultiple choice

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You are writing a query in Azure Cosmos DB for NoSQL to calculate the vector distance between a stored embedding and a query vector. You want to retrieve only the top 5 most similar documents. What is the correct structure of the LIMIT or TOP clause?

Question 19easymultiple choice

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Which property in an Azure Cosmos DB for NoSQL vector embedding policy defines whether the similarity calculation measures cosine, dotproduct, or euclidean distance?

Question 20hardmultiple choice

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You are designing an enterprise RAG application that stores vector embeddings in Azure AI Search. You want to ensure that if the primary Azure region goes down, your search service automatically fails over with minimal data loss. How should you design your geo-replication strategy?

Question 21mediummultiple choice

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You are provisioning an Azure AI Search service to support hybrid search, combining full-text lexical search and vector search. Which tier is the minimum required to support vector search capabilities at enterprise scale?

Question 22hardmultiple choice

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You are implementing a hybrid search solution in Azure AI Search that combines BM25 keyword search scores and HNSW vector search scores. By default, how does Azure AI Search normalize and combine these disparate score spaces before applying the RRF algorithm?

Question 23easymultiple choice

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You are storing embeddings generated by text-embedding-3-small in Azure Cosmos DB for NoSQL. Which indexing policy property must be explicitly configured to enable vector search queries on your container?

Question 24mediummultiple choice

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You are configuring an Azure AI Search index with vector fields. You need to ensure that filtering operations (such as matching a category ID) are executed with 100% precision before or alongside the vector search. Which vector filter mode should you choose?

Question 25hardmultiple choice

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You are troubleshooting a high memory consumption issue on an Azure AI Search service hosting large vector indexes. Which index configuration choice contributes most directly to high memory pressure when using the HNSW algorithm?

Question 26easymultiple choice

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You are configuring a vector profile in Azure AI Search. Which three elements must be correctly linked together to define a complete vector search configuration?

Question 27mediummulti select

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You are implementing vector search using Azure Cosmos DB for MongoDB (vCore). Which TWO distance metrics are valid options when creating a vector search index? Choose two.

Question 28hardmulti select

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You are designing a data ingestion pipeline that generates embeddings using Azure OpenAI and stores them in Azure AI Search. Which THREE components are required in the Azure AI Search index definition to support integrated vectorization? Choose three.

Question 29mediummultiple choice

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You are migrating a custom RAG solution to Azure. Your data pipeline generates text embeddings using Azure OpenAI text-embedding-ada-002 and stores them alongside relational metadata in Azure SQL Database. To perform vector similarity searches efficiently directly inside Azure SQL, which data type and extension should you leverage?

Question 30hardmultiple choice

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You are troubleshooting high latency in an Azure AI Search vector indexing pipeline. The source data is stored in Azure Blob Storage. You notice that the 'skillset' execution is the bottleneck. Which configuration change is most likely to reduce ingestion time?

Question 31easymulti select

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Which THREE data types or formats are commonly used to represent embeddings when preparing data for storage in a vector-enabled Azure service?

Question 32hardmultiple choice

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You are designing an Azure Cosmos DB for NoSQL container to store documents with vector embeddings. You need to perform vector search using the built-in VectorDistance function. The embeddings are 1536-dimensional and you expect millions of documents. You want to minimize latency while maintaining high recall. Which indexing policy should you configure?

Question 33mediummultiple choice

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You are using Azure AI Search to implement a hybrid search solution that combines keyword search and vector search. You want to ensure that the results are ranked by a combined relevance score. What should you configure?

Question 34hardmultiple choice

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You are managing an Azure Cosmos DB for NoSQL account that stores vector embeddings in a container. You need to reduce the cost of vector queries while maintaining acceptable recall. Which configuration change should you make?

Question 35easymultiple choice

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When configuring an Azure Cosmos DB for NoSQL container to store embeddings, what data type must the vector property be stored as within the JSON document?

Question 36mediummultiple choice

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You are configuring an Azure AI Search index schema via the REST API. You need to define a field that will hold a 1536-dimensional vector. Which field type must you assign to this field?

Question 37hardmultiple choice

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You are configuring semantic ranking in Azure AI Search to improve search relevance for your enterprise application. Which tier of Azure AI Search is required to enable semantic ranking?

Question 38mediummultiple choice

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You are building a hybrid search solution using Azure AI Search that combines BM25 full-text search with vector search. What is the name of the feature that intelligently combines and normalizes scores from different retrieval systems before presenting the final top results?

Question 39easymultiple choice

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Your company stores product catalog items in Azure Cosmos DB for NoSQL and needs to implement vector search for recommendation capabilities. Which indexing policy property must you add to enable vector indexing on a container?

Question 40mediummultiple choice

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You are writing a query in Azure Cosmos DB for NoSQL to retrieve documents based on vector similarity against a target embedding. Which built-in SQL function should you use in the ORDER BY clause?

Question 41easymultiple choice

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Which similarity metric measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude, and is commonly used in Azure AI Search?

Question 42mediummultiple choice

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You are using Azure OpenAI Service to generate embeddings for your documents before storing them in Azure Cosmos DB for NoSQL. The embedding model outputs vectors with a length of 1536. When defining the vector embedding policy in Cosmos DB, what value should you specify for the dimensions property?

Question 43hardmulti select

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You are designing an enterprise search architecture on Azure that requires hybrid search capabilities. Which THREE components or features must be configured to successfully execute a hybrid query with semantic ranking in Azure AI Search? (Choose three)

Question 44easymultiple choice

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Which distance metric in Azure Cosmos DB vector search calculates the shortest straight-line segment between two points in a multi-dimensional space?

Question 45mediummultiple choice

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You are designing the data architecture for an AI-powered document search application. You choose Azure AI Search because you need multi-lingual support, faceted navigation, and vector search. What is the maximum number of vector fields allowed per index in Azure AI Search?

Question 46mediummultiple choice

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You are configuring vector search in Azure Cosmos DB for NoSQL. You want to ensure that vectors are indexed using the DiskANN algorithm for high recall and fast search performance. Which index type should you specify in the vector embedding policy?

Question 47hardmulti select

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You are implementing a robust data layer architecture for an enterprise generative AI application. Which THREE factors should you evaluate when deciding between Azure AI Search and Azure Cosmos DB for vector search? (Choose three)

Question 48hardmultiple choice

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You are configuring data ingestion into an Azure AI Search index using an Azure AI Search indexer. The source data resides in Azure Blob Storage and consists of PDF documents. You want to chunk the documents and generate embeddings during ingestion without writing custom orchestration code. Which feature should you configure?

Question 49mediummulti select

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You are configuring vector search options in Azure Cosmos DB for NoSQL. Which TWO distance metrics are supported when defining a vector embedding policy? (Choose two)

Question 50hardmulti select

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When designing a hybrid search architecture that integrates Azure AI Search with an Azure OpenAI embedding model, which THREE challenges must you typically address regarding data management and retrieval? (Choose three)

Question 51mediummultiple choice

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You are optimizing search performance in Azure AI Search. You notice that vector queries are consuming excessive memory and slowing down. You decide to enable exhaustive k-NN fallback for specific queries. What does exhaustive k-NN do?

Question 52easymultiple choice

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In Azure Cosmos DB for NoSQL, which property in the container definition specifies the path to the property containing the vector array within each document?

Question 53mediummulti select

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When configuring an Azure AI Search vector profile, which TWO elements must be successfully referenced together to make the profile functional? (Choose two)

Question 54mediummultiple choice

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You are implementing a RAG solution where user queries are matched against an Azure AI Search vector index. You want to ensure that search results only return documents where a specific metadata field 'securityLevel' matches the user's clearance. How should you apply this filter?

Question 55easymultiple choice

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Which Azure AI Search feature allows you to re-order search results using advanced machine learning models trained by Microsoft to dramatically improve semantic relevance?

Question 56hardmultiple choice

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You are designing a data layer architecture in Azure Cosmos DB for NoSQL for an AI application that performs high-frequency writes and real-time vector searches. You need to understand how vector indexing impacts write operations. What is the primary trade-off?

Question 57mediummulti select

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When configuring an Azure AI Search vector index, which TWO parameters can be adjusted within the HNSW algorithm configuration to trade off between search speed, memory usage, and recall accuracy? (Choose two)

Question 58mediummultiple choice

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You are writing a Python application using the Azure Cosmos DB Python SDK to perform a vector search. You execute a parameterized SQL query containing VectorDistance(). What must be passed as a query parameter for the VectorDistance function to evaluate correctly against the container's vector property?

Question 59hardmultiple choice

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You are configuring a vector index in Azure AI Search and want to apply scalar quantization to reduce the memory footprint. What is the primary effect of scalar quantization on float32 vector data?

Question 60easymultiple choice

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Which Azure service provides a fully managed NoSQL database with native vector search capabilities and turnkey multi-region replication?

Question 61hardmulti select

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You are optimizing an Azure Cosmos DB for NoSQL vector search container. Which THREE properties must be correctly configured inside the vectorEmbeddingPolicy JSON object? (Choose three)

Question 62mediummultiple choice

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You are building a chat application with memory using Azure AI Search. You want to retrieve previous conversation turns that are semantically similar to the current user prompt. What search mode should you use if you want to execute both a vector search and a keyword search simultaneously and combine their results?

Question 63easymultiple choice

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Which property in an Azure Cosmos DB for NoSQL SQL query specifies the maximum number of vector search results to return?

Question 64mediummulti select

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When configuring vector search in Azure Cosmos DB for NoSQL, which TWO components must be defined within the container's indexing policy? (Choose two)

Question 65mediummultiple choice

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You are developing a RAG solution and need to evaluate the quality of your vector retrieval results from Azure AI Search. Which metric is commonly used to measure the proportion of relevant documents retrieved in the top-k results?

Question 66hardmulti select

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You are designing a secure enterprise generative AI platform using Azure AI Search and Azure OpenAI. Which THREE security practices should you implement to protect sensitive data and access? (Choose three)

Question 67hardmultiple choice

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You are designing a high-throughput RAG pipeline where documents are continuously ingested and queried. You choose Azure Cosmos DB for NoSQL as your vector store. How does Cosmos DB handle vector indexing updates when new documents are inserted?

Question 68easymultiple choice

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Which Azure AI Search API parameter allows you to specify a vector query alongside traditional keyword search terms when executing a hybrid search?

Question 69mediummulti select

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When configuring an Azure AI Search vectorizer for integrated vectorization, which TWO parameters or connections must be specified to link to an Azure OpenAI deployment? (Choose two)

Question 70hardmulti select

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You are designing a data layer architecture for an enterprise AI assistant that requires high-performance vector search, structured filtering, and multi-tenant isolation. Which THREE design patterns should you consider? (Choose three)

Question 71mediummultiple choice

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You are writing a query in Azure Cosmos DB for NoSQL that sorts results by vector similarity and also filters results by a category property. How must you order the clauses in your SQL statement?

Question 72easymultiple choice

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Which REST API HTTP method is used to create or update an index definition in Azure AI Search?

Question 73easymultiple choice

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Which tool in Azure Portal allows you to test search queries, inspect index schemas, and view JSON responses for Azure AI Search?

Question 74mediummulti select

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When configuring vector search in Azure AI Search, which TWO components are defined within the vectorSearch object of an index definition? (Choose two)

Question 75hardmulti select

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You are troubleshooting a scenario where hybrid queries in Azure AI Search are returning unexpected relevance rankings. Which THREE factors could influence the final score when using hybrid search with semantic ranking? (Choose three)

Question 76mediummulti select

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When designing an Azure AI Search index schema, which TWO properties must be defined for every field in the index? (Choose two)

Question 77mediummultiple choice

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You are designing an application that stores vector embeddings in Azure Cosmos DB for NoSQL. You want to ensure that vectors are stored securely and that all data at rest is encrypted using customer-managed keys (CMK). How is this achieved in Cosmos DB?

Question 78hardmultiple choice

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You are building an AI search solution in Azure AI Search. You need to ensure that when documents are deleted from the primary database, they are also automatically removed from the search index without requiring manual API calls. How should you configure the indexer?

Question 79mediummultiple choice

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You are configuring semantic ranking in Azure AI Search. You need to specify which fields in your index contain the title and content that the semantic ranker should analyze. Where do you configure these field mappings?

Question 80easymultiple choice

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Which distance metric in Azure Cosmos DB vector search calculates the inner product of two vectors, measuring both magnitude and angle?

Question 81hardmulti select

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You are optimizing an enterprise Azure Cosmos DB for NoSQL vector database. Which THREE administrative or architectural practices help ensure high availability and predictable performance? (Choose three)

Question 82hardmultiple choice

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You are designing a RAG solution where documents are dynamically updated in Azure AI Search. You notice that search results return outdated information immediately after document updates. What is the cause of this behavior?

Question 83hardmulti select

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You are evaluating data layer architectures for an enterprise AI application that requires both vector search and structured transactional data management. Which THREE criteria favor choosing Azure Cosmos DB over Azure AI Search as the primary database? (Choose three)

Question 84mediummultiple choice

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You are implementing vector search in Azure AI Search using the REST API. You want to execute a pure vector query without any keyword matching. What parameter must be omitted or left empty in your query request payload?

Question 85easymultiple choice

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Which Azure AI Search metric or tool helps administrators monitor query volume, latency, and throttling rates across search units?

Question 86hardmultiple choice

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You are designing an AI application architecture where embeddings are generated on-the-fly by an Azure Function and stored in Azure Cosmos DB for NoSQL. During peak load, the Azure Function times out due to rate limits (HTTP 429) from Azure OpenAI Service. What design pattern should you implement to make the ingestion pipeline resilient?

Question 87hardmulti select

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You are troubleshooting a performance degradation in an Azure Cosmos DB for NoSQL vector search application. Which THREE diagnostic steps or metrics should you inspect? (Choose three)

Question 88mediummultiple choice

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You are designing an AI search architecture that requires handling millions of documents with sub-second vector similarity queries. You choose Azure AI Search. How does Azure AI Search ensure high query throughput for vector indexes?

Question 89hardmultiple choice

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You are managing an Azure Cosmos DB for NoSQL database containing vector embeddings. You need to perform a migration of embeddings from one container to another without taking the application offline. Which Azure feature should you use?

Question 90hardmulti select

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You are designing a high-scale vector search architecture using Azure AI Search. Which THREE operational and architectural best practices should you follow to ensure optimal performance and cost-efficiency? (Choose three)

Question 91easymultiple choice

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Which Azure AI Search capability allows you to extract text from images and PDF documents during indexer execution using AI cognitive skills?

Question 92mediummultiple choice

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You are writing a Python script to query an Azure AI Search index using the azure-search-documents SDK. You want to retrieve both vector matches and keyword matches using hybrid search. Which class should you instantiate to represent the vector query?

Question 93hardmultiple choice

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You are designing a high-availability RAG architecture where search queries must succeed even if an entire Azure region experiences an outage. You deploy Azure AI Search in a primary region and a secondary region. How should client applications handle failover?

Question 94mediummultiple choice

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You are configuring an Azure AI Search index where text fields are automatically analyzed using language-specific rules. Which property on an Edm.String field specifies the language analyzer (e.g., 'en.microsoft')?

Question 95hardmulti select

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You are designing a secure data layer architecture for financial documents using Azure Cosmos DB for NoSQL and Azure AI Search. Which THREE compliance and security controls should you implement? (Choose three)

Question 96easymultiple choice

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Which Azure service provides cloud-scale vector search backed by either HNSW or DiskANN algorithms alongside managed NoSQL capabilities?

Question 97hardmultiple choice

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You are troubleshooting a performance issue where an Azure AI Search indexer fails to complete within the scheduled execution window when processing large PDF documents. What is the most effective configuration change to resolve the timeout issue?

Question 98mediummulti select

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When configuring vector search in Azure AI Search, which TWO parameters must be defined when creating a vector profile? (Choose two)

Question 99hardmulti select

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You are designing a high-performance RAG data layer architecture using Azure AI Search. Which THREE indexing and retrieval strategies should you implement to maximize recall and precision? (Choose three)

Question 100easymultiple choice

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Which Azure AI Search field property indicates whether the contents of a field can be returned in search query result documents?

Question 101hardmultiple choice

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You are designing an AI architecture that stores vector embeddings in Azure Cosmos DB for NoSQL. You need to implement role-based access control (RBAC) so that specific application microservices can only execute read-only queries against the database without having permission to write or delete documents. How should you configure this?

Question 102mediummulti select

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When configuring vector search in Azure AI Search, which TWO components are required to successfully define and execute a vector search query? (Choose two)

Question 103hardmulti select

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You are designing a disaster recovery and backup strategy for Azure Cosmos DB for NoSQL storing vector embeddings. Which THREE backup options or capabilities are available? (Choose three)

Question 104mediummultiple choice

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You are building an AI search solution using Azure AI Search. You want to ensure that users searching for acronyms or synonyms receive relevant results even if the exact keyword does not appear in the document. Which feature should you configure?

Question 105mediummulti select

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When configuring vector search in Azure Cosmos DB for NoSQL, which TWO steps are required to prepare a container for vector search before inserting documents? (Choose two)

Question 106easymultiple choice

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You are configuring an Azure AI Search vector index for a retrieval-augmented generation (RAG) application. You need to select an algorithm for vector similarity search that balances recall speed with accuracy while allowing approximate nearest neighbor (ANN) search. Which vector search algorithm configuration should you select?

Question 107hardmulti select

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You are optimizing an enterprise search architecture using Azure AI Search. Which THREE mechanisms help control latency and resource consumption during heavy vector query loads? (Choose three)

Question 108hardmultiple choice

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You are designing a multi-tenant enterprise RAG system using Azure AI Search. Each tenant requires isolated document retrieval. Which feature should you implement to enforce data security while leveraging vector search?

Question 109easymultiple choice

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When storing vector embeddings in Azure Cosmos DB for MongoDB (vCore), which distance metric is natively supported when creating a vector search index?

Question 110mediummultiple choice

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You are integrating Azure OpenAI Service's text-embedding-ada-002 model with Azure AI Search. You want to automate the generation of embeddings directly inside the search service during data ingestion. Which component of Azure AI Search should you configure?

Question 111hardmultiple choice

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You are configuring vector search on an Azure AI Search index using a custom vectorizer that points to an external embedding endpoint. Which authentication method is recommended and natively supported for securely connecting Azure AI Search to the Azure OpenAI endpoint?

Question 112easymultiple choice

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You are designing an application that stores vector embeddings in Azure SQL Database. Which data type should you use to store vector data natively in modern Azure SQL?

Question 113mediummultiple choice

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You are querying an Azure Cosmos DB for NoSQL container using vector search. You execute a query using the VectorDistance() system function. Which query structure is required to properly execute this vector search?

Question 114hardmultiple choice

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You are designing a data architecture on Azure where embeddings are generated offline by an Azure Databricks pipeline and stored in Azure AI Search. You want to minimize ingestion latency and optimize throughput when uploading millions of pre-computed vectors. Which client library API pattern should you use?

Question 115mediummultiple choice

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You are implementing a multi-modal AI application that searches across both text descriptions and product images stored in Azure AI Search. Which feature allows you to query both modalities within a unified search index?

Question 116mediummulti select

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You are configuring an Azure AI Search vector index. Which TWO parameters are part of the HNSW algorithm configuration settings? Choose two.

Question 117hardmulti select

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You are designing an enterprise search architecture that combines Azure AI Search and Azure OpenAI. Which THREE strategies help optimize hybrid search relevance and retrieval quality? Choose three.

Question 118hardmultiple choice

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You are optimizing query performance for an Azure AI Search index experiencing high query volumes with vector search. You need to scale out query processing capabilities without increasing index build time. Which action should you perform?

Question 119mediummulti select

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When configuring vector search in Azure Cosmos DB for NoSQL, which TWO properties must be defined within the container's vector embedding policy? Choose two.

Question 120hardmulti select

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You are troubleshooting slow query performance in an enterprise Azure AI Search vector deployment. Which THREE architectural or configuration adjustments can improve query throughput and latency? Choose three.

Question 121mediummulti select

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You are planning a data layer architecture for an AI application on Azure that requires both relational transactional storage and vector search capabilities. Which TWO Azure database services natively support vector data types and similarity search functions? Choose two.

Question 122mediummultiple choice

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You are developing an enterprise retrieval-augmented generation (RAG) application using Azure AI Search. You need to configure a vector index to store dense vector embeddings alongside traditional text fields. Which property within the Azure AI Search index definition must be explicitly configured with a vector search profile to enable HNSW (Hierarchical Navigable Small World) algorithm indexing?

Question 123hardmultiple choice

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Your team is implementing hybrid search in Azure AI Search combining full-text keyword search and vector search. Users report that certain exact product serial numbers are missing from the semantic ranking results. Which query parameter should you adjust to balance the influence of BM25 text search scores against vector cosine similarity scores?

Question 124easymultiple choice

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You are storing vector embeddings generated by Azure OpenAI in Azure Cosmos DB for NoSQL. Which built-in SQL function must you use within your query to compute vector distance when executing a similarity search?

Question 125mediummultiple choice

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You are configuring vector indexing in Azure Cosmos DB for MongoDB (vCore). You need to choose an indexing type for your vector property to balance query performance and recall accuracy for high-dimensional data. Which vector index type should you select for approximate nearest neighbor (ANN) search?

Question 126hardmultiple choice

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You are optimizing storage costs and search latency for a massive Azure AI Search vector index containing 50 million records, each with 3,072-dimensional embeddings from text-embedding-3-large. Which Azure AI Search feature should you implement to reduce memory footprint while maintaining acceptable recall?

Question 127easymultiple choice

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When designing a data layer architecture for an AI application using Azure AI Search, what is the primary role of an integrated vectorizer?

Question 128hardmulti select

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You are designing the data storage and indexing strategy for a multi-tenant AI search solution on Azure. Which TWO architectural practices should you implement to optimize vector search performance and tenant isolation in Azure AI Search?

Question 129mediummulti select

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Your enterprise application architecture incorporates Azure Cosmos DB for NoSQL as the primary operational data store and Azure AI Search as the vector index for semantic search. Which THREE mechanisms can you use to keep the Azure AI Search vector index synchronized with changes in Azure Cosmos DB?

Question 130mediummultiple choice

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You are designing an application that requires low-latency vector search for a high-traffic AI chatbot. You need to ensure the vector index remains performant as the dataset grows into millions of documents. Which index configuration in Azure AI Search should you implement to optimize for speed over absolute recall accuracy?

Question 131hardmulti select

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When configuring vector search algorithms in Azure AI Search, you can tune several parameters associated with the Hierarchical Navigable Small World (HNSW) graph. Which THREE parameters are configurable within an HNSW algorithm profile?

Question 132mediummultiple choice

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Your application uses Cosmos DB for NoSQL to store product metadata and vectors. You need to perform a vector search that combines similarity scores with specific filter criteria on the 'category' field. How should you define your index policy to support this?

Question 133easymultiple choice

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You are configuring a hybrid search in Azure AI Search. You want to combine results from a vector search and a keyword-based search. Which feature is specifically designed to normalize scores from these different retrieval methods into a single ranked list?

Question 134mediummultiple choice

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You are building a RAG application using Azure AI Search. You want to ensure that the search results provided to the LLM are as relevant as possible by leveraging a re-ranking model. Which service must be enabled to utilize the 'Semantic Ranker'?

Question 135mediummultiple choice

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You have a large collection in Azure Cosmos DB for NoSQL and want to perform a vector search. The application is currently hitting 'Request Rate Too Large' (429) errors during vector search queries. What is the most effective way to mitigate this?

Question 136easymultiple choice

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When designing an Azure AI Search solution, you need to store high-dimensional embeddings. Which field type must be used to store these vector representations correctly?

Question 137hardmultiple choice

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You are configuring a Vector Index in Azure AI Search. You need to ensure that the vector similarity search is performed using the Cosine distance metric. Where is this configuration specified?

Question 138easymultiple choice

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You need to ensure that your vector data in Azure AI Search is protected at rest. Which setting should you verify?

Question 139mediummultiple choice

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You are evaluating Cosmos DB for NoSQL vs. Azure AI Search for a vector search application. Your application requires strict ACID compliance for metadata updates along with vector search. Which should you choose and why?

Question 140mediummulti select

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You are designing a vector search solution using Azure AI Search. Which THREE of the following are necessary steps to configure an index to support vector search?

Question 141hardmulti select

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When migrating data into Azure AI Search for vector search, you encounter issues with ingestion. Which TWO of the following are valid reasons why an indexer might fail to ingest vector data?

Question 142mediummulti select

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Which THREE features are common to both Azure AI Search and Azure Cosmos DB for NoSQL when performing vector search?

Question 143mediummulti select

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Your team is choosing between using Azure AI Search or Cosmos DB for NoSQL for a new project. Which TWO factors are key considerations that favor Azure AI Search?

Question 144mediummultiple choice

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You are building a RAG solution using Azure AI Search. Documents are updated frequently, and you need each document's vector embedding to be regenerated automatically whenever its text changes, without writing custom code to detect changes. Which Azure AI Search feature should you configure?

Question 145hardmultiple choice

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You manage an Azure Cosmos DB for NoSQL container that stores product documents with a vector embedding property. You need to query for products similar to a given embedding while also filtering by a category partition key. Which query approach should you use?

Question 146mediummultiple choice

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You are building a RAG solution on Azure AI Search. Documents and their embeddings are updated frequently, and you need the vector index to reflect changes within seconds without rebuilding the entire index. Which indexer behavior should you configure?

Question 147hardmultiple choice

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You are designing an Azure Cosmos DB for NoSQL container to store vector embeddings for a product recommendation engine. Queries will filter on category and then perform vector similarity search. You need to minimize latency and ensure the index supports both operations efficiently. Which indexing policy should you implement?

Question 148mediummultiple choice

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You are implementing vector search in Azure AI Search for a document retrieval system. You need to ensure that the vector search returns the most relevant results for a query embedding. Which similarity metric should you configure in the vector search profile?

Question 149hardmultiple choice

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You are managing an Azure Cosmos DB for NoSQL account that stores vector embeddings. You need to query the database using the VectorDistance function to find the top 10 most similar items to a given vector. The container has a vector index configured. Which query syntax should you use?

Question 150mediummulti select

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You are configuring vector search in Azure AI Search for a knowledge mining solution. You need to ensure that the vector search queries return results efficiently and accurately. Which TWO components must be defined in the index schema? (Choose two.)