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| Section | Weight | Objectives |
|---|---|---|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
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NEW QUESTION # 28
Hotspot Question
A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.
The team working on the model must ensure the following:
- Changes in input data distribution are detected.
- Appropriate actions are triggered when predefined thresholds are
exceeded.
You need to configure monitoring to meet the requirements.
Which configuration should you use 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 # 29
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 # 30
Hotspot Question
A team manages an Azure Machine Learning workspace to train and register machine learning models.
Previous model versions must be retained for audit and rollback purposes but must not be used for new deployments.
You need to manage model versions.
What should you do 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 # 31
Drag and Drop Question
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
Which four 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:
NEW QUESTION # 32
A team uses a hosted Git repository to store training code and pipeline definitions of a machine learning experiment.
The team must ensure that access to the repository is granted without requiring each developer to store personal access tokens on their machines.
Repository access must be secure and centrally managed to reduce credential spread.
You need to enable secure access between an Azure Machine Learning workspace and the repository.
What should you do?
Answer: D
Explanation:
Configuring the workspace to use a managed identity (or a centralized service principal/workspace identity) is the correct approach to securely and centrally manage repository access without spreading personal access tokens.
No Developer Credentials: Developers do not need to generate, share, or store personal access tokens (PATs) on their local machines.
Centralized Management: Access permissions are bound to the cloud workspace resource rather than individual user accounts.
Reduced Credential Spreading: Eliminates the risk of leaked tokens when team members leave or compromise local workstations.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication
NEW QUESTION # 33
......
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