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| Section | Objectives |
|---|---|
| Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production |
| Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries |
| Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads |
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NEW QUESTION # 33
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 # 34
A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?
Answer: C
Explanation:
Conditional logic in pipelines allows automated decisions based on evaluation metrics, ensuring only models that meet performance thresholds are registered and deployed. This maintains full automation. Manual approval steps interrupt automation and are not scalable in continuous integration and deployment workflows.
NEW QUESTION # 35
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 # 36
An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?
Answer: C
Explanation:
Azure Private Endpoints are network interfaces that connect your Azure ML workspace to your Virtual Network using a private IP address from the VNet ' s address space. Once a private endpoint is created and DNS is configured, all traffic to the workspace - including the Studio UI, REST API, and SDK calls - travels entirely over Microsoft ' s private backbone rather than the public internet. The workspace ' s public endpoint can then be completely disabled. Azure Firewall (option A) filters traffic at the network layer but still requires the workspace to have a public IP. Network Security Groups (option C) control traffic within VNets but cannot block the public endpoint of a PaaS service. Service endpoints (option D) keep traffic on the Azure backbone but the workspace still has a public-facing address. Private endpoints are the only option that fully removes the public network presence.
Microsoft Learn Reference Topic: Configure a private endpoint for Azure Machine Learning workspace
NEW QUESTION # 37
Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that uses Azure AI Search to retrieve documents from an indexed knowledge base.
The system must support the following retrieval requirements:
- Queries that include exact policy identifiers must return matching
documents even when semantic similarity is low.
- Natural-language questions must prioritize semantically relevant
documents even when keywords are not an exact match.
You need to configure the retrieval approach to meet the requirements.
How should you configure the retrieval behavior for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 38
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