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| Section | Objectives |
|---|---|
| Topic 1: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 2: Design and implement generative AI solutions | - Large language model integration
|
| Topic 3: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Topic 4: Implement secure and scalable AI systems | - Security and governance
|
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NEW QUESTION # 110
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Clone an existing Azure Machine Learning workspace to create additional environments.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
This action is best because it fulfills all of your operational requirements:
Consistency & Controlled Manner:
Infrastructure as Code (IaC) via Bicep ensures that dev, test, and prod environments are configured identically, eliminating configuration drift.
Source Control: Storing the Bicep template in a Git repository satisfies the requirement that each environment must be updated through source control.
Automated & Repeatable: Orchestrating the deployment using GitHub Actions fully automates the workflow, allowing it to be executed reliably every time a change is merged.
Auditable: Git commit histories combined with GitHub deployment logs provide a comprehensive, compliant audit trail of exactly who modified the infrastructure and when.
Incorrect:
* Clone an existing Azure Machine Learning workspace to create additional environments.
* Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
NEW QUESTION # 111
A team deploys a generative AI application that uses a model deployed in Microsoft Foundry.
The application must support latency monitoring under production load.
You need to enable performance observability.
Which three 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:
Microsoft ' s observability guidance for Azure AI Foundry applications describes a three-stage activation sequence. First, enable Tracing in the Microsoft Foundry project settings before deployment - Tracing instruments the application ' s LLM calls with OpenTelemetry-compatible spans that capture timing data for each step in the flow. Second, deploy the application to a production endpoint so that real traffic flows through the instrumented code path - without actual production traffic, there is no latency data to observe.
Third, configure Azure Monitor and Application Insights to receive, aggregate, and visualize the telemetry emitted by Tracing. Azure Monitor ' s metrics explorer and Application Insights ' performance views display p50, p95, and p99 latency distributions over time, enabling the team to identify latency regressions and set alert thresholds. This sequence - instrument, deploy, visualize - is the canonical Microsoft path to production AI performance observability.
Microsoft Learn Reference Topic: Monitor generative AI applications with Azure Monitor and Microsoft Foundry Tracing
NEW QUESTION # 112
Hotspot Question
You manage an Azure Machine Learning workspace. You configure an automated machine learning regression training job by using the Azure Machine Learning Python SDK v2.
You configure the regression job by using the following script:
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:
Box 1: Yes
Yes, the automated machine learning (AutoML) training job will terminate early if the primary metric score stops improving after a specific number of iterations.
According to the official Microsoft Azure Machine Learning SDK v2 documentation, setting enable_early_termination = True activates the early stopping policy for the overall experiment.
How Early Termination Works in AutoML v2
The internal early stopping logic operates on a built-in schedule to avoid premature termination:
First 20 iterations: No early stopping takes place (these serve as landmarks).
From the 21st iteration onward: The early stopping window activates.
Termination trigger: The job will automatically stop if the primary evaluation score fails to improve across 10 consecutive iterations.
Box 2: Yes
Yes, a maximum of five trials can run at the same time.
The limit comes from the code settings.
The max_concurrent_trials = 5 line tells Azure to run up to five trials at once.
Box 3: No
No, a single AutoML trial cannot run for 60 minutes before it is terminated.
Parameter Breakdown
In the Azure Machine Learning Python SDK v2, the set_limits() method handles timeouts via two distinct parameters:timeout_minutes = 60: This sets the maximum duration for the entire AutoML job (the experiment as a whole), including data preparation, featurization, and all training trials combined.
trial_timeout_minutes: This parameter governs the maximum time allowed for an individual trial (a single model training run) before termination.
Conclusion
Because trial_timeout_minutes is omitted from your script, it defaults to its standard system value (which is typically 20 minutes for tabular datasets). Therefore, an individual trial will time out much earlier than 60 minutes. Additionally, since the entire job terminates at 60 minutes, it is physically impossible for a single trial to consume the full 60 minutes without forcing the termination of the remaining concurrent runs.
Box 4: No
No, the AutoML trial cannot take up to 1 month before it terminates.
Based on the script parameters, the job will terminate after a maximum of 60 minutes.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.automl.regressionjob?view=azure-python
NEW QUESTION # 113
You are authoring a notebook in Azure Machine Learning studio.
You must install packages from the notebook into the currently running kernel. The installation must be limited to the currently running kernel only.
You need to install the packages.
Which magic function should you use?
Answer: D
Explanation:
The correct choice is %pip . Azure Machine Learning compute instances can contain multiple Jupyter kernels, so package installation must target the interpreter associated with the notebook ' s currently active kernel . Microsoft explicitly recommends the %pip or %conda IPython magic commands for this purpose because these commands install packages into the environment associated with the running kernel.
For example, a notebook cell can contain:
%pip install scikit-learn
This ensures that the installed package becomes available to code executed by that notebook kernel.
By contrast, !pip invokes a shell command. Microsoft specifically warns against using !pip or !conda when the intention is to modify only the current notebook kernel, because shell-level package commands can reference environments or packages outside the active kernel.
%load has an entirely different purpose: it loads Python source code into a notebook cell and does not perform package installation.
Therefore, when package installation must be isolated to the active Azure Machine Learning notebook kernel,
%pip is the appropriate magic command .
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning development environments, notebook kernels, dependency management, and compute instances.
NEW QUESTION # 114
You need to isolate training workloads while remaining cost-aware to address Fabrikam Inc.'s issues, constraints, and technical requirements.
What should you implement?
Answer: B
Explanation:
Imagine a hospital analytics firm with data scientists who kick off dozens of training jobs throughout the week. During peak hours, five jobs compete for the same GPU cluster and fail or queue for hours. On quiet nights, that cluster sits completely idle, burning money. Managed compute targets with autoscaling solve both problems: the cluster scales out automatically when multiple jobs arrive simultaneously and scales back to zero when idle. Option A (single shared cluster) is exactly the resource-contention problem Fabrikam already has. Option B (fixed-size cluster) wastes money during off-peak hours. Option C (dedicated per-experiment clusters) eliminates contention but is prohibitively expensive for a cost-conscious healthcare firm.
Autoscaling managed compute is the cloud-native answer to variable workload demand.
Microsoft Learn Reference Topic: Azure Machine Learning compute targets - Configure autoscale for compute clusters
NEW QUESTION # 115
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