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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
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NEW QUESTION # 43
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.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset1.csv
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* python script.py --training_data ${{inputs.training_data}}
The scipt is named script.py.
For the parameter use ${{inputs.training_data}}
Incorrect:
* python script.py --training_data dataset1.csv
* python script.py dataset1.csv
* python train.py --training_data training_data
Note: Read a TabularDataset, Example
In the Input object, specify the type as AssetTypes.MLTABLE, and mode as InputOutputModes.DIRECT:
* Details omitted*
job = command(
code="./src", # Local path where the code is stored
*-> command="python train.py --inputs ${{inputs.input_data}}",
inputs=my_job_inputs,
environment="<environment_name>:<version>",
compute="cpu-cluster",
)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2
NEW QUESTION # 44
A team deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production.
When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems.
You need to configure the monitoring solution to meet the requirements.
Which configuration should you associate with each requirement as a first step? 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:
Microsoft ' s documentation on Azure Machine Learning model monitoring describes a layered alerting architecture. At the base layer, Azure ML model monitors compute drift, prediction, and data quality metrics on a scheduled basis and publish results as Azure Monitor metrics. To notify stakeholders, you create an Azure Monitor alert rule that watches these metrics and fires an action group when a threshold is breached.
Action groups support email, SMS, push notifications, and webhook calls. To initiate automated retraining, the webhook call in the action group targets an Azure ML pipeline ' s REST endpoint, triggering a retraining run. Alternatively, Azure Event Grid subscriptions on AML workspace events can route model-quality events to Azure Functions that start pipelines. The separation of monitoring, alerting, notification, and remediation is intentional, allowing each component to be updated independently.
Microsoft Learn Reference Topic: Set up model monitoring for data and model quality - Azure Machine Learning model monitoring
NEW QUESTION # 45
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team requires generative AI to automatically evaluate every pull request of an agentic application and fail the build if safety thresholds are exceeded.
You need to automate evaluations as part of CI.
What should you configure?
Answer: C
Explanation:
The correct solution is a GitHub Actions workflow that executes Microsoft Foundry evaluations as part of the CI process . Microsoft provides an AI agent evaluation GitHub Action specifically for incorporating Foundry agent evaluations into CI/CD workflows. The action can invoke the agent against an evaluation dataset, execute configured evaluators-including safety evaluators-and publish evaluation results before a change reaches production.
For pull-request gating, the workflow can be configured to run whenever relevant application files change.
Evaluation commands can also enforce explicit thresholds and return a non-zero exit code when those thresholds are not satisfied. Microsoft documents evaluation gating through options such as --fail-on pass- rate= < threshold > or --fail-on any-failure; a non-zero result causes the CI job to fail, preventing unsafe changes from being promoted.
A blocklist or content filter provides runtime content protection but does not automate pull-request evaluation.
A retrieval chunking strategy affects RAG retrieval quality, not CI safety gates.
Therefore, the required mechanism is GitHub Actions integrated with Foundry evaluation runs and safety thresholds .
Study Guide Reference: Implement generative AI quality assurance and observability - automated evaluations, CI/CD quality gates, safety evaluators, GitHub Actions, and pre-production validation.
NEW QUESTION # 46
Your ML pipeline contains independent feature engineering steps that currently execute sequentially, increasing overall runtime. You want to optimize execution without modifying logic.
What is the BEST solution?
Answer: A
Explanation:
Parallel execution allows independent pipeline steps to run simultaneously, reducing total execution time without altering logic. This is more efficient than increasing compute resources, which may raise costs without guaranteeing proportional performance improvements.
NEW QUESTION # 47
Hotspot Question
A regression model is deployed to a production online endpoint and is actively serving predictions.
Business stakeholders report that the model's prediction quality has changed.
The endpoint remains available and responsive.
You need to determine which metric to examine to validate each quality concern.
Which metric should you review for each concern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 48
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