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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q70-Q75):

NEW QUESTION # 70
A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

Answer: B

Explanation:
Cold starts occur when instances scale down to zero and must restart to handle new requests.
Setting a minimum instance count ensures that at least one instance is always running, reducing latency spikes. Increasing timeouts does not address the root cause of cold start delays.


NEW QUESTION # 71
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics .
What should you do?

Answer: A

Explanation:
Define the evaluation dataset with input prompts and expected outputs covering both quality and safety scenarios. Configure built-in evaluators such as Coherence, Fluency, Groundedness, and Content Safety alongside custom Python evaluator functions for domain-specific safety checks. Create a GitHub Actions workflow that triggers on pull requests or merges to the main branch, calling the Azure AI evaluation SDK to run the evaluation job automatically. Add a quality gate by parsing the evaluation results and using a conditional step to fail the workflow when thresholds are not met. Configure GitHub branch protection rules to require the evaluation workflow to pass, preventing merging or deploying code that fails the quality gate.
Enabling application tracing (option A) and monitoring latency (option C) are runtime monitoring tools, not release gates. Reviewing results manually after deployment (option B) directly contradicts the requirement to eliminate manual evaluation.
Microsoft Learn Reference Topic: Automate AI evaluation in CI/CD with GitHub Actions - Microsoft Foundry evaluation integration


NEW QUESTION # 72
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area . NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
A system requesting predictions synchronously during customer interactions needs sub-second responses, while a system submitting files with millions of records can tolerate minutes of processing time. For real-time synchronous serving, a Managed Online Endpoint provisions a persistent always-on container behind an HTTPS REST endpoint that returns predictions within milliseconds. For large-batch asynchronous scoring, a Batch Endpoint accepts a data asset input, distributes scoring across a compute cluster, and writes results back to storage. Online endpoints support auto-scaling based on request volume and traffic splitting. Batch endpoints are invoked on-demand or on a schedule, automatically provisioning and de-provisioning compute, keeping costs low for intermittent large jobs. Each deployment type is purpose-built for its usage pattern and should not be swapped.
Microsoft Learn Reference Topic: Deploy and score models with managed online endpoints and batch endpoints - Azure Machine Learning


NEW QUESTION # 73
Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 74
A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

Answer: C,E

Explanation:
To host generative AI workloads in a Microsoft Foundry environment with strictly private communication and no public network exposure, you must configure a Managed Virtual Network (Managed VNet) with specific isolation settings and disable all public inbound access.
[A]
Enable Managed Virtual Network Isolation
During the creation of your Azure AI Foundry hub, navigate to the Networking tab.
Select the Private with Approved Outbound isolation mode. This mode ensures that all outbound traffic from the managed compute resources is restricted to only the destinations you explicitly approve, such as dependent Azure resources.
Once enabled, this isolation mode cannot be disabled.
[E]
Disable Public Inbound Access
In the Networking tab of your Foundry resource, set Public network access to Disabled.
This action blocks all traffic from the public internet, ensuring the resource is only accessible through private connections.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/managed-virtual-network


NEW QUESTION # 75
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