AI-200 Cert - Realistic Latest Developing AI Cloud Solutions on Azure Exam Testking Free PDF Quiz

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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Connect to and consume Azure services- Integrate Azure services
  • 1. Azure messaging and eventing
  • 2. Third-party SDKs
  • 3. Serverless integration patterns
  • 4. Event-driven architectures
  • 5. Azure SDKs
Develop containerized solutions on Azure- Implement containerized applications
  • 1. Deploy AI workloads in containers
  • 2. Implement scalable hosting patterns
  • 3. Manage containerized compute environments
Develop AI solutions by using Azure data management services- Work with Azure data platforms for AI workloads
  • 1. Vector databases
  • 2. Azure data management services
  • 3. Data integration for AI applications
Secure, monitor, troubleshoot Azure solutions- Operate AI cloud solutions
  • 1. Troubleshooting Azure solutions
  • 2. Monitoring and observability
  • 3. Performance optimization
  • 4. Security and secret management

>> AI-200 Cert <<

Free PDF Quiz AI-200 - Developing AI Cloud Solutions on Azure Updated Cert

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Microsoft Developing AI Cloud Solutions on Azure Sample Questions (Q47-Q52):

NEW QUESTION # 47
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 # 48
You are developing an application that uses a Python API to perform similarity queries against Azure Database for PostgreSQL. The application creates a new database connection for every request.
During peak traffic, the application intermittently fails to open new database sessions, logs indicate that the maximum number of connections have been reached.
You need to configure the connection pooling strategy to reduce connection setup overhead and maximize reuse for the high-concurrency workload.
What should you configure? To answer. select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

onnection pooling technology: PgBouncer. Pooling mode: Transaction pooling mode.
Detailed Explanation: Azure Database for PostgreSQL Flexible Server supports PgBouncer to reduce connection setup cost and protect the server from excessive client sessions. For a high-concurrency API whose requests do not require a server session to remain pinned between transactions, transaction pooling maximizes reuse by returning the server connection to the pool after each transaction. Session pooling holds a server connection for the lifetime of the client session and therefore provides less multiplexing under the connection-pressure scenario described.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | PgBouncer in Azure Database for PostgreSQL


NEW QUESTION # 49
You are developing several microservices to run on Azure Container Apps.
You need to monitor and diagnose the microservices.
Which features should you use? To answer, select the appropriate feature in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
View console logs from a container in near real-time: Log streaming
* Debug the microservice from inside the container: Container console
For near-real-time troubleshooting, use Log streaming . Azure Container Apps supports streaming both system logs and container console logs directly through the Azure portal or Azure CLI. Microsoft documents that container console logs originate from the application ' s stdout and stderr streams, and the log-streaming feature can display them in near real time. This is the correct feature when developers need immediate visibility into container output without waiting for log ingestion into a monitoring workspace.
To debug the application from inside the running container , use the Container console . Azure Container Apps allows operators to connect interactively to a container by using the portal console or the az containerapp exec command. From there, they can run commands through shells such as bash or sh, inspect files, test network connectivity, examine environment variables, and troubleshoot runtime behavior directly in the container context.
Azure Monitor metrics are appropriate for aggregated performance measurements rather than interactive container debugging. Azure Monitor Log Analytics is useful for historical queries and centralized analysis, but Microsoft notes that ingested logs can have a delay of several minutes, so it is not the best choice for near- real-time console output.
Study Guide references: Azure Container Apps # Log streaming; Container console; observability; Azure Monitor integration; interactive troubleshooting.


NEW QUESTION # 50
Hotspot Question
You are developing an event-driven system that reacts to new or updated documents in Azure Cosmos DB for NoSQL.
The application must process new or updated documents as they appear in the container by using a change feed processor. The application must also maintain processor state between runs.
You need to configure the components required for stateful change feed processing.
Which components should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Monitored container.
To read new or updated documents from the source, you need to configure the monitored container. The change feed processor listens to this container for any changes and sequentially retrieves the sorted list of modified documents.
Box 2: Lease Container
Lease Container is a separate container that acts as state storage. It maintains processing checkpoints across multiple worker instances, which allows your system to resume operations from where it left off.
Box 3: Latest version mode
To process new or updated documents, you need to use the Latest version mode of the change feed.
Latest version mode is the default change feed mode. It provides only the most recent version of each changed item (inserts and updates) in the exact order they were modified. If an item is updated multiple times in quick succession, the processor will only receive the final state after the latest update.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/change-feed-processor


NEW QUESTION # 51
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 using a parameterized query. You must minimize the possibility of SQL injection.
How should you update the implementation to execute the SQL statement safely? 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: Modify SQL: use a parameterized query. Bind input: pass the ID as an argument. Execute:
supply the parameter tuple to the SDK/driver method.
Detailed Explanation: The security boundary is created by separating SQL syntax from user-supplied values.
The statement should contain a parameter placeholder rather than concatenated input, and the document ID should be supplied separately through the database driver's parameter mechanism. The driver then performs the correct protocol-level binding and escaping. Manually escaping quotation marks is error-prone and does not provide the same injection resistance as true parameterization.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector similarity search with Azure PostgreSQL


NEW QUESTION # 52
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