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| Section | Objectives |
|---|---|
| Topic 1: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search |
| Topic 2: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Topic 3: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
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NEW QUESTION # 143
Drag and Drop Question
A recommendation system stores 3 million embeddings in Azure Database for PostgreSQL.
During peak hours, P95 similarity-query latency increases, and the cache hit ratio drops significantly.
You suspect that the vector index working set no longer fits in memory, resulting in more disk reads.
You need to scale server resources and validate the outcome.
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:
Step 1: Before peak hours, run a fixed workload to capture baseline P95 latency and capture EXPLAIN ANALYZE for the vector query Establishing a baseline is crucial for performance tuning. Running EXPLAIN ANALYZE provides deep insight into whether the query is actually using the vector index (like HNSW or IVFFlat) or falling back to a slow sequential scan.
Step 2: Scale to a tier with more memory per vCore
Since the problem states that the vector index working set no longer fits in memory, scaling to a tier with higher memory (such as Memory-Optimized tiers) directly solves the root cause by allowing the RAM to fully cache the vector index, eliminating slow disk reads.
Step 3: Re-run the same benchmark and compare P95 and P99 latency
To validate the outcome of your scaling operations, you must run the identical benchmark workload and compare high-percentile latencies (P95, P99) against your baseline to prove the bottleneck was resolved.
Step 4: Review memory pressure and cache hit ratio during peak hours and compare them to the baseline This step confirms the success of the resource scaling. A successful remediation will show a significantly increased cache hit ratio and reduced memory pressure/disk I/O compared to the baseline metrics.
Reference:
https://www.pgedge.com/blog/ai-features-in-pgadmin-ai-insights-for-explain-plans
NEW QUESTION # 144
Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
You need to perform a one-time analysis of logs collected from AKS, ACA, and Azure Function apps according to the business requirements.
What should you use?
Answer: B
Explanation:
Business Requirements
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
A KQL query is the correct tool to use for a one-time, cross-resource analysis to correlate telemetry across AKS, Azure Container Apps (ACA), and Azure Functions.
Cross-Resource Querying: KQL (Kusto Query Language) allows you to use the union operator to combine tables from different workspaces or applications effortlessly.
Correlation Capabilities: You can join logs using standard cloud fields like operation_Id,
_ResourceId, or custom headers to track a single request across your entire architecture.
On-Demand Analysis: It requires no setup time, making it ideal for a one-time investigation directly inside the Azure Monitor Log Analytics interface.
Reference:
https://oneuptime.com/blog/post/2026-02-16-how-to-perform-cross-workspace-queries-in-azure-log-analytics/view
NEW QUESTION # 145
Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
You need to address the known issue resulting from vector similarity queries.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Known Issues: RU consumption spikes during vector similarity queries.
To address the RU (Request Unit) consumption spikes during vector similarity queries in Azure Cosmos DB, you should perform the following two steps:
Modify the indexing precision of the vector fields
Change the vector index type from flat to quantizedFlat or diskANN
Indexing Precision Tuning: Modifying parameters like vector quantization (e.g., configuring quantizedByteSize) alters the size and precision of the stored vector elements. Lowering the precision decreases memory usage and index size, allowing faster searches that consume far fewer RUs at the expense of marginal recall accuracy.
Index Type Modification: A standard flat index conducts a brute-force k-nearest neighbors (kNN) exact search across every document. This requires vast computational overhead and causes RU spikes as your dataset grows. Transitioning to quantizedFlat or diskANN leverages compression techniques and advanced graph-traversal algorithms to perform approximate nearest neighbor (ANN) searches, dropping query latency and compute costs significantly.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/vector-search-performance-tips
NEW QUESTION # 146
You are configuring sampling for a distributed application that sends traces to Azure Monitor. The solution must:
* Preserve upstream sampling decisions across distributed traces
* Capture all spans during local testing.
* Sample 10 percent of traces in production.
You need to apply the appropriate sampling configuration for each requirement.
What should you do? 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:
Verified answer: Preserve parent sampling decisions: ParentBasedSampler. Capture all spans locally:
AlwaysOnSampler. Sample 10% in production: TraceIdRatioBasedSampler(0.1).
Detailed Explanation: Parent-based sampling propagates the sampling decision from the upstream parent, preventing broken sampling decisions across a distributed trace. AlwaysOnSampler records every span, which is suitable for local test environments where complete trace visibility is more important than telemetry volume. TraceIdRatioBasedSampler with 0.1 selects approximately ten percent of traces based on trace identifiers, providing deterministic fixed-ratio sampling for production. A batch span processor and an exporter control processing/export, not the sampling decision itself.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | OpenTelemetry sampling in Azure Monitor | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 147
A recommendation system stores 1 million embeddings in Azure Database for PostgreSQL During peak hours. P95 similarity-query latency increases, and the cache hit ratio drops significantly.
You suspect that the vector index working set no longer fits in memory, resulting in more disk reads.
You need to scale server resources and validate the outcome.
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:
NEW QUESTION # 148
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