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Microsoft AI-200 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Plan and manage Azure AI solutions | - Plan security and compliance requirements - Monitor and optimize AI solutions - Select appropriate Azure AI services |
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions - Implement knowledge mining with Azure AI Search |
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
You need to configure a connection string for the partner-facing service according to the technical requirements.
What should you use?
- A. Azure Container Registry Helm chart package
- B. Dockerfile ENV instructions
- C. Azure Key Vault references in App Service settings
- D. GitHub secrets
Correct Answer: C 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
You need to implement the semantic retrieval workflow for the recommendation engine to meet the technical and performance requirements of Fabrikam Inc.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
* Define a table schema with vector and metadata columns.
* Load embedding vectors and associated product metadata.
* Configure a Hierarchical Navigable Small World (HNSW) index on the embedding vector columns.
* Perform a similarity search using a WHERE clause and the < = > operator.
The workflow must first establish a PostgreSQL schema containing both the pgvector embedding column and the product metadata required for filtering. The embeddings and their associated metadata are then bulk- loaded. This ordering is important because Microsoft recommends loading data before creating vector indexes ; creating the index afterward provides faster ingestion and a more optimal index layout.
After loading, configure an HNSW index on the embedding column. HNSW is an approximate-nearest- neighbor index supported by pgvector and provides a strong speed/recall tradeoff for low-latency vector retrieval. Microsoft specifically documents HNSW for efficient cosine-distance searches in Azure Database for PostgreSQL.
Finally, execute the retrieval query so that mandatory product metadata constraints are applied through a WHERE clause , while vector similarity is evaluated with the < = > cosine-distance operator . This satisfies Fabrikam ' s requirement to calculate similarity only for eligible products.
A B-tree index is not an ANN vector index, and increasing Redis memory does not implement PostgreSQL semantic retrieval.
Study Guide references: Azure Database for PostgreSQL # pgvector, HNSW indexing, bulk-load optimization, vector similarity operators, metadata-filtered retrieval.
You need to deploy a batch embedding workload according to the planned application architecture.
What should you use?
- A. az aks commands
- B. YAML-formatted files
- C. kubectl run and create commands
- D. XML-formatted files
Correct Answer: B 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?
- A. Inspect the pod events and container logs.
- B. Scale the deployment to more replicas.
- C. Drain and reboot the node hosting the pod.
- D. Decrease the initialDelaySeconds for the container liveness probe.
Correct Answer: A 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
Reference the Azure Key Vault without requiring any changes to the Java code.
Dynamically add and remove instances of the Azure Functions host based on the number of incoming application events.
Ensure that instances are perpetually warm to avoid any cold starts.
Connect to a VNet.
Authentication to the Azure Key Vault instance must be removed if the Azure Function application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Step 1: Create the Azure Functions app with a Consumption plan type.
Use the Consumption plan for serverless.
Step 2: Create a system-assigned managed identity for the application.
Create a system-assigned managed identity for your application.
Key Vault references currently only support system-assigned managed identities. User-assigned identities cannot be used.
Step 3: Create an access policy in Key Vault for the application identity.
Create an access policy in Key Vault for the application identity you created earlier. Enable the " Get " secret permission on this policy. Do not configure the " authorized application " or applicationId settings, as this is not compatible with a managed identity.
Reference:
https://docs.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
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