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| Section | Objectives |
|---|---|
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Select appropriate Azure AI services - Plan security and compliance requirements |
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Implement Azure AI solutions | - Implement computer vision solutions - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI |
>> AI-200 Reliable Test Topics <<
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NEW QUESTION # 23
Case Study 1 - Fabrikam Inc.
Background
Fabrikam Inc. is a global retail analytics company that provides AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure.
The platform ingests transaction data, generates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security.
The solution must meet strict performance, scalability, and security requirements.
Current environment
Application architecture
The Recommendation engine is a customer-facing HTTP API running as a containerized Python application. The engine is deployed to Azure Container Apps (ACA).
Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
Semantic retrieval uses metadata filtering combined with vector similarity search.
Azure Managed Redis is used as a caching layer.
Front-end and API workloads are deployed to Azure Container Apps (ACA).
Batch model retraining workloads run in Azure Kubernetes Service (AKS).
Container and CI/CD
Container images are stored in Azure Container Registry (ACR).
CI/CD uses ACR Tasks to build images on commit.
ACA environments support revision management.
AKS workloads are deployed by using Kubernetes manifest files stored in Git.
Monitoring
Logs are collected in Azure Monitor.
Teams inspect container logs and Kubernetes events when troubleshooting.
Developers write KQL queries to analyze latency spikes.
Business requirements
Customer experience: Maintain a seamless, low-latency recommendation experience for end- users, even during unpredictable seasonal traffic spikes.
Operational cost efficiency: Minimize compute expenditures by deallocating resources during periods of inactivity and by preventing runaway scaling costs.
Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction.
Security and compliance: Adhere to a Zero Trust security model by eliminating long-lived credentials and centralizing the management of all sensitive secrets.
Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers.
Technical requirements
Performance: Semantic search latency must remain under 200 milliseconds at peak load.
Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints.
Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization.
Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete.
Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries upon metadata updates.
Identity: Use managed identities for all service-to-service and service-to-database authentication.
Plain-text credentials in configuration files are strictly prohibited.
Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy.
Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero.
CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits.
Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.
Hotspot Question
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Azure Service Bus scaler
An Azure Service Bus scaler needs to be set up to meet this requirement.
Queue-Depth Metrics: To trigger scaling based strictly on queue depth, the orchestration layer (such as Azure Container Apps or AKS using KEDA) requires a scaler that can natively communicate with the message broker and monitor metrics like active message count.
Scale-to-Zero Support: Unlike standard resource scalers, an Azure Service Bus scaler enables event-driven batch workloads to spin up consumers when messages arrive and scale all the way down to zero instances when the queue is completely empty.
Box 2: Minimum replicas = 0.
You need to set Minimum replicas = 0.
Setting the minimum replicas to 0 enables the system to scale down to zero instances when there are no messages left in the queue, completely deallocating resources and eliminating idle compute costs.
Box 3: Maximum replicas = 10
To prevent uncontrolled burst scaling during your batch retraining workloads, you need to set maximum replicas = 10.
Enforcing Upper Bounds: Allowing the system to determine the maximum replica gives the underlying autoscaler (such as KEDA or the Horizontal Pod Autoscaler in Azure Kubernetes Service) the freedom to scale out infinitely or up to large default platform limits. This directly risks uncontrolled burst scaling when a massive batch queue is processed.
Resource and Cost Protection: Hard-coding a ceiling (like maximum replicas = 10) ensures that the batch workload cannot consume more cluster resources than allocated, preventing resource starvation for customer-facing recommendation APIs and keeping operational costs predictable.
Reference:
https://azure.github.io/aca-dotnet-workshop/aca/09-aca-autoscale-keda/
https://kserve.github.io/website/docs/reference/crd-api
NEW QUESTION # 24
You are developing an application that must extract structured field data (invoice number, total, vendor name) from scanned invoices in multiple layouts.
You need a solution that requires no custom model training.
What should you use?
Answer: C
Explanation:
Use the Azure AI Document Intelligence prebuilt invoice model . The prebuilt invoice model is specifically designed to analyze invoices and extract structured fields and line items without requiring an organization to train a custom model. Microsoft documents support for fields including InvoiceId , VendorName , InvoiceTotal , InvoiceDate, CustomerName, AmountDue, tax information, addresses, and invoice line items.
Because the model is pretrained to recognize common invoice structures, it can process invoices that use different layouts and formats while returning normalized structured output. This directly satisfies the requirement to extract invoice number, total, and vendor information from scanned documents while minimizing development and training effort.
The Azure AI Vision Read API performs OCR and can extract printed or handwritten text, but it does not provide invoice-specific semantic field extraction. Custom named entity recognition would require training and is designed for text entity extraction rather than document-layout understanding. A Document Intelligence custom neural model can handle organization-specific forms and layouts, but it requires custom model training, directly violating the requirement.
Therefore, the prebuilt invoice model provides the required combination of OCR, document understanding, structured extraction, and zero custom training.
Study Guide references: Azure AI Document Intelligence # prebuilt invoice model; structured field extraction; OCR; invoice fields and line items.
NEW QUESTION # 25
A python web API uses OpenTelemetry for tracing. The call downstream service function makes an outbound HTTP request by using the requests library. The following code is the only OpenTelemetry configuration in the application:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selectin is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 26
A RAG application's retrieval step is returning documents that are topically related but not precisely relevant to the user's question. You need to improve retrieval precision without retraining any model. What should you do?
Answer: A
Explanation:
Azure AI Search's semantic ranker re-scores the initial retrieval results using a secondary language-understanding model, improving precision by pushing more contextually relevant results to the top without any custom model training.
NEW QUESTION # 27
Drag and Drop Question
A Python API retrieves a document from Azure Database for PostgreSQL by using a SQL statement. The API accepts the document ID from user input. The current implementation inserts the document ID directly into the SQL statement.
You need to secure the SQL statement execution by minimizing the possibility of SQL injection.
How should you modify the current implementation? 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:
Box 1: Use a parameterized query
To secure your SQL statement against SQL injection, you must replace direct string insertion with parameterized queries (also known as prepared statements).
Box 2: Pass the ID as an argument
The current implementation inserts the document ID directly into the SQL statement.
Box 3: Supply the parameter tuple to the SDK method
Supply parameter tuples: Pass the user input as a separate tuple or list argument into your database driver's execution method (e.g., cursor.execute(query, (document_id,))).
Reference:
https://www.stackhawk.com/blog/finding-and-fixing-sql-injection-vulnerabilities-in-flask-python/
NEW QUESTION # 28
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