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| Section | Objectives |
|---|---|
| Connect to and consume Azure services | - Integrate Azure services
|
| Develop containerized solutions on Azure | - Implement containerized applications
|
| Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 77
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.
Answer:
Explanation:
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.
NEW QUESTION # 78
A development team wants to compare multiple prompt variations against the same test dataset and visualize which prompt performs best. What should they use in Azure AI Foundry?
Answer: B
Explanation:
Prompt flow variants allow you to define multiple versions of a prompt/node, run them against the same evaluation dataset, and compare results side-by-side to identify the best-performing variant.
NEW QUESTION # 79
You need to troubleshoot connectivity failures between microservices running in AKS.
Which troubleshooting actions should you perform? To answer, move the appropriate action to the correct troubleshooting scenario. You may use each action 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.
Answer:
Explanation:
Explanation:
* Service fails to communicate with another service: Inspect Kubernetes service endpoints.
* Pod restarts repeatedly: Inspect container logs.
* Readiness probe failures are observed: Inspect Pod descriptions.
For a service-to-service communication failure , first inspect the Kubernetes service endpoints . Microsoft' s AKS troubleshooting guidance recommends verifying that the destination pod IP address and application port appear as endpoints for the Kubernetes Service. An empty or incorrect endpoint list commonly indicates a selector/label mismatch, incorrect port configuration, or unavailable backend pods.
For a pod that restarts repeatedly , inspect the container logs , including the previous container instance when necessary. Microsoft specifically recommends kubectl logs < pod > --previous for workloads that repeatedly restart because it exposes application errors and termination messages from the container immediately before the restart.
For readiness probe failures , inspect the Pod description by using kubectl describe pod. The description exposes the configured health probes, pod conditions, container state, and associated probe-failure events, allowing you to determine whether the probe path, port, timing, or application readiness behavior is incorrect.
Microsoft recommends reviewing kubectl describe output when pods are not Ready or health probes are failing.
Therefore, the correct mapping is service endpoints # container logs # Pod descriptions .
Study Guide references: AKS troubleshooting; Kubernetes Services and endpoints; container logging; Pod health; readiness/liveness probes; kubectl describe and kubectl logs.
NEW QUESTION # 80
You are developing an Azure Function that calls external APIs by providing an access token for the API. The access token is stored in a secret named token in an Azure Key Vault named mykeyvault.
You need to ensure the Azure Function can access the token. Which value should you store in the Azure Functions app configuration?
Answer: C
NEW QUESTION # 81
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Store the API key as an App Service application setting configured through the Azure Portal.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* Store the API key in Azure Key Vault and reference it from an App Service application setting.
Storing the API key in Azure Key Vault and referencing it via App Service application settings is the recommended, secure approach. This strategy completely removes sensitive credentials from your GitHub repository and Git commit history while injecting them safely into your container environment at runtime.
Incorrect:
* Embed the API key as a hardcoded environment variable in the Dockerfile.
* Store the API key as a GitHub repository secret.
* Store the API key as an App Service application setting configured through the Azure Portal.
Reference:
https://www.qservicesit.com/full-stack-applications-on-azure
NEW QUESTION # 82
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