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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions |
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
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NEW QUESTION # 82
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You are preparing a container image for deployment to production.
The container image build and deployment process must ensure the following:
*The image is uniquely versioned.
*Secure authentication is used when pushing the image to Azure Container Registry (ACR).
*The image is stored in ACR for deployment.
You need to ensure that the container image build-and-push process meets the requirements.
Which action should you perform for each requirement? To answer, move the appropriate actions to the correct requirements. 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:
* Ensure that the image is versioned: Use a unique tag for the image.
* Allow secure push to ACR: Authenticate to ACR by using Microsoft Entra ID.
* Store the image in the registry for deployment: Push the image to ACR.
For production deployments, each image should receive a unique tag so that a specific build can be identified and deployed consistently. Microsoft recommends unique tags for deployments because each pushed image receives a distinct version reference instead of reusing mutable tags such as latest. Typical unique-tagging schemes include build IDs, timestamps, or source-control identifiers.
Before pushing the image, authenticate to Azure Container Registry by using Microsoft Entra ID . Microsoft recommends Azure identity-based authentication, such as through az acr login, rather than relying on long- lived registry administrator credentials. Appropriate Azure RBAC/data-plane permissions must also permit the identity to push artifacts.
Finally, use a push operation such as docker push < registry > .azurecr.io/ < repository > : < tag > to upload the tagged image and its layers to ACR. Once pushed successfully, the image is stored in the registry and can be referenced by downstream deployment services.
Build the image locally is not mapped to any stated requirement. Building may be necessary in some workflows, but it does not itself provide unique versioning, authentication, or storage in ACR.
Study Guide references: Azure Container Registry # image tagging and versioning; Microsoft Entra authentication; pushing container images; repositories and artifacts.
NEW QUESTION # 83
Drag and Drop Question
You have a newly provisioned Azure subscription. You are designing a custom Event Grid workflow for AI inference events.
You need to implement the Event Grid components to support routing of high-confidence events to a downstream processor.
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.
Answer:
Explanation:
Explanation:
Step 1: Register a provider
Because this is a newly provisioned Azure subscription, you must register the Microsoft.EventGrid resource provider before you can deploy any Event Grid components.
Step 2: Create a custom topic
Since you are routing AI inference events originating from your application, you need to create a custom topic to act as the endpoint where these events are sent.
Step 3: Create an event subscription
You must create an event subscription to define the routing rules. This is where you configure filtering to ensure only high-confidence events are directed to your downstream processor.
Reference:
https://www.c-sharpcorner.com/article/building-event-driven-ai-workflows-with-azure-event-grid-and-net/
NEW QUESTION # 84
You need to configure the Redis integration for the Recommendation API.
Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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:
* Comply with the defined expiration policy for recommendation results: Configure Time to Live on each cache key.
* Support the data integrity and freshness requirement for catalog updates: Publish invalidation events to a Redis channel.
* Meet the search latency target for recurring product requests: Implement cache-aside pattern with lazy loading.
The 10-minute expiration requirement should be implemented by configuring a Time to Live (TTL) on each Redis cache key. Redis automatically removes a key after its assigned expiration interval, ensuring recommendation results cannot remain cached indefinitely. Microsoft documents expiration as a standard mechanism for preventing cached data from becoming stale.
For catalog changes, Fabrikam explicitly requires a reactive invalidation mechanism . Publishing invalidation events to a Redis Pub/Sub channel allows application components to notify subscribers immediately when product metadata or pricing changes. Redis Pub/Sub broadcasts messages to channel subscribers, which can then invalidate affected cache entries instead of waiting for TTL expiration.
For repeated recommendation queries, implement the cache-aside pattern with lazy loading . The application first checks Redis; on a cache hit, results are returned from the low-latency in-memory cache. On a miss, the application retrieves data from PostgreSQL, places the result in Redis, and returns it. Microsoft identifies cache-aside as an appropriate pattern for frequently read data and explicitly notes that Azure Managed Redis provides low-latency, high-throughput access.
Redis RDB persistence is unnecessary for these requirements because the cache is an optimization layer rather than the authoritative data store.
Study Guide references: Azure Managed Redis # cache-aside pattern; TTL/expiration; Pub/Sub; cache invalidation and data freshness.
NEW QUESTION # 85
An ACA app processes messages from an Azure Storage queue.
The app must scale automatically based on messages in a specific Azure Storage queue by using a Kubernetes Event-driven Autoscaler (KEDA) custom scale rule.
You need to configure the required scale rule values.
Which two values should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
[A]
QeueName: The exact name of the specific Azure Storage queue your app is polling is required.
[C]
Why Trigger Type is Required
When configuring a custom scale rule in Azure Container Apps (ACA), the platform utilizes KEDA underneath. Because a custom rule can connect to many different event sources (such as Kafka, Redis, or Azure Storage), you must explicitly define the Trigger type (e.g., azure-queue) so KEDA knows which specific scaler to initiate.
Reference:
https://techcommunity.microsoft.com/blog/fasttrackforazureblog/container-apps-a-practical-scaling-with-azure-queue-scale-rule/3722075
NEW QUESTION # 86
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column.
The schema must support vector similarity search and reliable date filtering.
You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
To meet the requirements, store embeddings in a pgvector column and use a typed timestamp column for created_at. These two steps enable high-dimensional vector similarity operations and allow efficient, reliable date-based filtering.
Store embeddings in a pgvector column: Enables the database to store and query high- dimensional vector embeddings using native similarity operators.
Use a typed timestamp column for created_at: Ensures accurate date-and-time comparisons, indexing, and dependable performance when filtering queries by creation date.
Reference:
https://learn.microsoft.com/en-us/training/modules/implement-vector-search-azure-database-postgresql/
NEW QUESTION # 87
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