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| Section | Weight | Objectives |
|---|---|---|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
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NEW QUESTION # 123
You must ensure that PII (personally identifiable information) such as names and phone numbers is automatically redacted from support ticket transcripts before they are stored. What should you use?
Answer: B
Explanation:
Azure AI Language's PII detection feature identifies and can redact categories of sensitive information (names, phone numbers, SSNs, etc.) from unstructured text, which is the purpose- built service for this scenario.
NEW QUESTION # 124
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
Answer:
Explanation:
Explanation:
Verified Answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | AKS scaling overview | Kubernetes services in AKS
NEW QUESTION # 125
You are reviewing an Azure Function app that processes incoming order requests for a company. The function must:
* Accept order submissions from an external client application.
* Require controlled access for security
* Return a response containing the processed request payload.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Yes; No; Yes; No.
Detailed Explanation: `auth_level=FUNCTION` means invocation requires a function key unless a stronger platform authentication layer is configured, so the first statement is true. The route explicitly permits POST, not GET. The function reads `req.get_body()` and returns that value in the HttpResponse, so the response contains the request body. No schema, type, required-field, or other payload validation is performed before the response is constructed, making the final statement false.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions HTTP trigger
NEW QUESTION # 126
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 # 127
Drag and Drop Question
You are designing Azure Functions for three different backend workloads.
Each workload requires one of the following dispatching models:
- Return an immediate response to a client.
- Process background work from a queue.
- Run code on a fixed schedule.
You need to select the trigger for each requirement.
Which triggers should you select? To answer, move the appropriate triggers to the correct requirements. You may use each trigger 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: HTTP trigger
An HTTP trigger is the best choice for returning an immediate response to a client.
Direct connection: The client makes a standard web request and waits on the same connection.
Low latency: It avoids the delays introduced by routing messages through queues or storage.
Two-way communication: It natively supports returning payload data and HTTP status codes (e.g., 200 OK).
Box 2: Queue Storage Trigger
Azure Queue Storage Trigger or Azure Service Bus Queue Trigger is the best choice to process background work from a queue.
Azure Queue Storage Trigger:
Best for simple, low-cost message queues with high throughput and straightforward FIFO (first-in, first-out) processing needs.
Automatic Scaling: Scales out instances dynamically based on the number of messages waiting in the queue.
Reliability: Automatically handles poison messages and retries failed executions if configured correctly.
Cost Efficiency: Consumes zero compute resources when the queue is empty (especially under the Consumption plan).
Box 3: Timer trigger
An Azure Function with a timer trigger is the standard and ideal choice for running code on a fixed schedule. It acts as a serverless cron job that automatically manages execution intervals, scales automatically, and includes built-in locking mechanisms to prevent duplicate executions across multiple instances.
Reference:
https://levelup.gitconnected.com/request-driven-service-vs-event-driven-service-4bf04d642843?gi=c0bdcb16db5a
https://codilime.com/blog/enabling-first-in-first-out-pattern-microsoft-azure-service-bus-queues-topics/
NEW QUESTION # 128
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