Microsoft AI-200 Q&A - in .pdf

  • Exam Code: AI-200
  • Exam Name: Developing AI Cloud Solutions on Azure
  • Updated: Sep 12, 2026
  • Q & A: 144 Questions and Answers
  • PDF Price: $59.99
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Microsoft AI-200 Q&A - Testing Engine

  • Exam Code: AI-200
  • Exam Name: Developing AI Cloud Solutions on Azure
  • Updated: Sep 12, 2026
  • Q & A: 144 Questions and Answers
  • Install on multiple computers for self-paced, at-your-convenience training.
  • PC Test Engine Price: $59.99
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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Topic 1: Connect to and consume Azure services- Integrate Azure services
  • 1. Third-party SDKs
  • 2. Serverless integration patterns
  • 3. Event-driven architectures
  • 4. Azure SDKs
  • 5. Azure messaging and eventing
Topic 2: Develop AI solutions by using Azure data management services- Work with Azure data platforms for AI workloads
  • 1. Azure data management services
  • 2. Data integration for AI applications
  • 3. Vector databases
Topic 3: Develop containerized solutions on Azure- Implement containerized applications
  • 1. Deploy AI workloads in containers
  • 2. Implement scalable hosting patterns
  • 3. Manage containerized compute environments
Topic 4: Secure, monitor, troubleshoot Azure solutions- Operate AI cloud solutions
  • 1. Monitoring and observability
  • 2. Security and secret management
  • 3. Performance optimization
  • 4. Troubleshooting Azure solutions

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question #1

Hotspot Question
You have an Azure Functions app using the Consumption hosting plan for a company. The app contains the following functions:

You plan to enable dynamic concurrency on the app. The company requires that each function has its concurrency level managed separately.
You need to configure the app for dynamic concurrency.
Which file or function names should you use? To answer, select the appropriate values in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
* File name: host.json
* Function name: f3
Azure Functions dynamic concurrency is enabled at the function-app host level in the host.json file . The concurrency configuration section contains the dynamicConcurrencyEnabled property. Setting this value to true enables the Functions host to dynamically learn and adjust concurrency levels for supported triggers rather than requiring fixed manual limits.
Although dynamic concurrency is enabled globally, Microsoft explicitly states that the learned concurrency level is managed independently for each individual function . This allows a resource-intensive function to operate at a lower concurrency level while a lightweight function in the same app can execute with greater concurrency, protecting host health while maximizing throughput.
Of the functions listed, f3 , which uses an Azure Queue Storage trigger , supports dynamic concurrency.
Microsoft currently documents dynamic concurrency support for Azure Queue Storage, Azure Blob Storage, and Azure Service Bus triggers , subject to the required extension versions. HTTP and Timer triggers do not participate in this dynamic-concurrency model.
Therefore, configure dynamic concurrency in host.json , and f3 is the function whose concurrency will be dynamically managed.
Study Guide references: Azure Functions # Concurrency; Dynamic concurrency; host.json; Queue Storage trigger concurrency.

Question #2

You have an Azure Service Bus namespace with a partitioned queue named queue1.
You plan to send a large number of messages through queue1 over the next few weeks. The order of messages will be random. You must minimize the possibility of message transmission interruption by transient failures of individual partitions.
You need to use the optimal configuration of the partition key in the messages.
Which configuration should you use?

  • A. Enable sessions. Set the partition key of messages to the session ID value.
  • B. Leave the partition key value as null.
  • C. Enable sessions. Ensure that the partition key is different from the session ID value.
  • D. Set the partition key of messages to the message ID value.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

You are implementing semantic retrieval for a chatbot.
Embeddings are already stored in Redis. However, vector similarity queries do not return matches.
You need to resolve the vector similarity search issue.
What should you do?

  • A. Enable geo-replication.
  • B. Reset expiration on each read.
  • C. Create an HNSW vector index on the embedding field.
  • D. Set a 24-hour Time to Live (TTL) on embedding keys.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #4

You need to configure the database resources for the Azure Database for PostgreSQL instance.
How should you complete the configuration to meet the business and technical requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
* Meet the 200-ms semantic search latency requirement: Increase compute vCores.
* Optimize the environment for high-dimensional pgvector index residency: Increase memory allocation.
* Support the continuous ingestion of transaction-based embeddings: Enable storage autoscale.
For the strict sub-200-ms vector-search latency target, increasing compute vCores is the appropriate choice.
Vector similarity operations are computationally intensive, and additional CPU capacity improves mathematical throughput and parallel query execution. Microsoft's pgvector guidance emphasizes query-plan optimization, ANN indexes such as HNSW, and sufficient compute resources when optimizing vector workloads.
For high-dimensional pgvector index residency , increase memory allocation . HNSW provides strong query-performance characteristics but consumes more memory than IVFFlat. Keeping frequently accessed vector index structures in memory minimizes disk access and materially improves latency. Microsoft explicitly notes that HNSW requires more memory while providing a better speed/recall tradeoff.
For the continuous ingestion of millions of embeddings, enable storage autoscale . Azure Database for PostgreSQL Flexible Server can automatically increase allocated storage as capacity approaches configured thresholds, avoiding an out-of-storage condition as data volumes grow. Microsoft recommends storage autogrow for workloads whose storage demand can increase dynamically.
Increasing max_connections does not directly improve vector computation or index residency, read replicas primarily scale reads, and backup retention does not address ingestion capacity.
Study Guide references: Azure Database for PostgreSQL Flexible Server # pgvector performance optimization; compute and memory sizing; HNSW indexing; storage autogrow.

Question #5

You are implementing an Azure solution that uses Azure Cosmos DB and the latest Azure Cosmos DB SDK.
You add a change feed processor to a new container instance.
You attempt to read a batch of 100 documents. The process fails when reading one of the documents. The solution must monitor the progress of the change feed processor instance on the new container as the change feed is read. You must prevent the change feed processor from retrying the entire batch when one document cannot be read.
You need to implement the change feed processor to read the documents.
Which features should you use? To answer, move the appropriate features to the correct requirements. Each feature may be used once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

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