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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Security and governance
|
| Topic 2: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 3: Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 150
Drag and Drop Question
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
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:
Step 1: Select a metric to measured
You need to choose a baseline performance or fairness metric (such as accuracy rate, precision, or demographic parity) along which the dashboard evaluates the model's overall behavior and cross-group disparities.
Step 2: Select a model feature to evaluated
You must specify a sensitive feature (such as age, gender, or race) to split the data into different subgroups and evaluate potential disparities across them.
Step 3: Select a binary classification or regression model
The Fairlearn package is specifically designed to assess and mitigate unfairness in binary classification and regression tasks. It does not natively support clustering or multiclass models for group fairness assessments in this wizard.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
NEW QUESTION # 151
Hotspot Question
You manage a Microsoft Foundry project.
You plan to build a RAG solution.
The solution must include two models:
- One for text output, named Model1. This model must resemble human
language and read naturally.
- One for creating embeddings, named Model2. This model must maximize
the retrieval of relevant results (high recall) while minimizing
irrelevant or incorrect matches (high precision).
You need to compare different models by using benchmarking metrics to select the appropriate models for Model1 and Model2.
Which benchmarking metric should you select for each model? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 152
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 in the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
Answer: A
Explanation:
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
* Create prompt variants and compare their outputs in the Evaluation experience.
* Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Note:
In Azure AI Foundry, you can capture and compare these metrics by enabling Tracing and using the Bulk Test feature. This allows you to systematically evaluate different prompt variants against a common dataset.
Steps to Evaluate and Compare Prompt Variants
*-> 1. Enable Tracing
Navigate to your Prompt Flow project.
Locate the Tracing toggle at the top of the flow authoring page.
Switch it to On.
This ensures every execution captures latency, token counts, and node-level inputs/outputs.
2. Create Prompt Variants
Within your flow, identify the LLM node you want to test.
Click Variants to create multiple versions of your prompt (e.g., Variant_0, Variant_1).
This allows you to test different instructions or few-shot examples side-by-side.
3. Run a Bulk Test (Evaluation)
4. Analyze the Results
Reference:
https://www.linkedin.com/pulse/streamlining-generative-ai-development-azure-foundry-tracing-taneja-mbwze
NEW QUESTION # 153
Hotspot Question
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
- name="mlcluster1"
- size="STANDARD_DS3_v2"
- min_instances=1
- max_instances=4
- tier="dedicated"
The cost of the compute resources must be minimized when a workload is active or idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: tier
Set tier to low_priority.
Lower your compute cluster cost with low priority VMs
Use any of these ways to specify a low-priority VM:
from azure.ai.ml.entities import AmlCompute
cluster_low_pri = AmlCompute(
name="low-pri-example",
size="STANDARD_DS3_v2",
min_instances=0,
max_instances=2,
idle_time_before_scale_down=120,
tier="low_priority",
)
ml_client.begin_create_or_update(cluster_low_pri).result()
Note: You may also to use low-priority VMs to run some or all of your workloads. These VMs don't have guaranteed availability and may be preempted while in use. You'll have to restart a preempted job.
Using Azure Low Priority Virtual Machines allows you to take advantage of Azure's unused capacity at a significant cost savings. At any point in time when Azure needs the capacity back, the Azure infrastructure will evict Azure Low Priority Virtual Machines. Therefore, Azure Low Priority Virtual Machines are great for workloads that can handle interruptions. The amount of available capacity can vary based on size, region, time of day, and more. When deploying Azure Low Priority Virtual Machines, Azure will allocate the VMs if there's capacity available, but there's no SLA for these VMs. An Azure Low Priority Virtual Machine offers no high availability guarantees. At any point in time when Azure needs the capacity back, the Azure infrastructure will evict Azure Low Priority Virtual Machines.
Incorrect:
* Size
Decrease of size would affect maximum amount of compute resources available to the workloads run on the cluster.
* max_instances
Decrease of max_instances would affect maximum amount of compute resources available to the workloads run on the cluster.
Box 2: min_instances
Use min_instances=0
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster
NEW QUESTION # 154
You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
What should you recommend?
Answer: B
Explanation:
MLflow is the industry-standard open-source platform for experiment tracking, and Azure Machine Learning has first-class native integration with it. When you use MLflow within an Azure ML job, parameters, metrics, and artifacts are automatically logged to the run history of the AML workspace, making every run reproducible and comparable. Option A (AML job output logs) only captures console output and lacks structured parameter and metric logging. Option C (Application Insights logs) is designed for application- level telemetry, not ML experiment metadata. Option D (Azure Monitor alerts) is a reactive notification tool, not a tracking system. MLflow ' s autologging capability means that for common frameworks such as scikit- learn, XGBoost, and PyTorch, parameters and metrics are captured without a single line of custom code, directly answering Fabrikam ' s requirement for consistent experiment tracking.
Microsoft Learn Reference Topic: Track ML experiments with MLflow - Azure Machine Learning MLflow integration
NEW QUESTION # 155
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