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| Section | Objectives |
|---|---|
| Implement Azure AI solutions | - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement computer vision solutions |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| 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 # 74
You have an existing AKS cluster and a container image stored in Azure Container Registry.
You must deploy a new version by using a Deployment manifest that supports rolling updates without interrupting traffic.
You need to perform a rolling update by using a manifest file.
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:
NEW QUESTION # 75
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.
Answer:
Explanation:
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.
NEW QUESTION # 76
Your Azure AI Search index must support both exact keyword matches on product SKUs and semantic similarity search on product descriptions in a single query. What should you configure?
Answer: D
Explanation:
Hybrid search in Azure AI Search combines traditional keyword (BM25) search over text fields with vector similarity search over embedding fields in a single query, giving both exact-match precision (SKUs) and semantic recall (descriptions).
NEW QUESTION # 77
Your application must classify uploaded product images into one of 40 custom categories specific to your business (e.g., proprietary part numbers). What should you use?
Answer: C
Explanation:
Prebuilt Vision models recognize general objects/scenes but not business-specific categories.
Custom Vision allows you to train a classifier on your own labeled images for domain-specific categories such as proprietary part numbers.
NEW QUESTION # 78
Drag and Drop Question
You are developing a solution for a hospital to support the following use cases:
- The most recent patient status details must be retrieved even if
multiple users in different locations have updated the patient record.
- Patient health monitoring data retrieved must be the current version
or the prior version.
- After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.
You provision an Azure Cosmos DB for NoSQL database and set the default consistency level for the database account to Strong.
You set the value for Indexing Mode to Consistent.
You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.
Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Strong
Strong consistency must be used at the query level to meet this requirement.
Linearizability Guarantee: Strong consistency ensures that a read operation always returns the most recent committed version of an item.
Global Consensus: Because multiple users in different locations are updating the patient record, any reader across any region must wait for the updates to be fully committed globally before reading. This prevents any user from reading stale or out-of-order data, which is critical for patient status details.
Box 2: Bounded Staleness
To minimize latency and maximize availability while ensuring that retrieved patient health monitoring data is either the current version or the immediate prior version, you should override the consistency level at the query level to Bounded Staleness.
Version Guarantee: Bounded Staleness ensures that reads are not older than a specific time window or a configured number of versions K versions behind the latest write). Setting the version staleness to 1 (K=1 )guarantees that the data read is either the current version or the prior version.
Lower Latency: Bounded Staleness offers significantly lower latency than the account's default Strong consistency. Strong consistency requires a global majority acknowledgment before a read or write completes, increasing latency.
Higher Availability: By backing down from Strong consistency to Bounded Staleness for this query, you reduce the strict synchronization requirements, thereby minimizing impacts on availability during localized network disruptions.
Box 3: Eventual
Eventual consistency is the correct consistency level to use at the query level for this scenario.
Maximizes Performance: Eventual consistency offers the lowest latency and highest availability among all consistency levels.
Matches Scenario Needs: The billing record is final and no longer changing, so there is no risk of reading stale, in-progress updates.
Optimizes Costs: Eventual consistency queries consume half the Request Units (RUs) compared to Strong or Bounded Staleness queries.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
NEW QUESTION # 79
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