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| Section | Objectives |
|---|---|
| Design and implement generative AI solutions | - RAG (Retrieval Augmented Generation) solutions
|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
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NEW QUESTION # 18
A data science team registers an MLflow model in Azure Machine Learning.
The model must support low latency predictions and automatically scale based on incoming request volume.
You need to deploy the model.
Which deployment option should you use?
Answer: B
Explanation:
The best deployment option is an Azure Machine Learning Managed Online Endpoint.
This deployment model is specifically designed for low-latency, real-time predictions and supports native autoscaling based on incoming request volume.
Turnkey MLflow Support: You can deploy your registered MLflow model directly without writing a custom scoring script or defining a docker environment.
Low Latency: Built on high-performance infrastructure designed for real-time inference workloads.
Autoscaling: Automatically scales the number of virtual machine instances up or down based on metrics like CPU usage, memory, or request queue depth.
Traffic Splitting: Supports deploying multiple model versions under one endpoint to safely test new models using blue-green deployment strategies.
Reference:
https://www.interviews.chat/questions/azure-machine-learning-engineer
NEW QUESTION # 19
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 # 20
You need to configure an optimization method to meet Fabrikam Inc.'s technical requirements.
Which strategy should you apply first? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Domain specialization: Supervised fine-tuning
Poor response accuracy of a RAG-based solution: Apply prompt engineering For domain specialization , Supervised Fine-Tuning (SFT) is the correct first strategy. Microsoft describes SFT as the foundational fine-tuning technique for training a model from labeled input-output pairs , and specifically identifies domain specialization as one of its principal use cases. Microsoft also recommends starting with SFT for most customization projects because it supports task specialization, instruction following, style, and domain-specific behavior. Fabrikam already possesses evaluation data containing input- output pairs, which aligns directly with the SFT data model.
For poor RAG response accuracy , the first action is prompt engineering . The case explicitly requires advanced fine-tuning only when prompt engineering is insufficient. In a RAG system, prompt engineering determines how the model interprets retrieved context, constrains answers to grounding information, handles missing evidence, and formats responses. Microsoft notes that inadequate RAG prompting can produce false or incomplete answers even when retrieval returns appropriate content.
DPO is primarily appropriate for alignment using preferred versus non-preferred responses, while RFT targets complex reward-based reasoning optimization. Neither is the initial technique for domain specialization in this requirement.
Study Guide Reference: Optimize generative AI systems and model performance - prompt engineering, RAG optimization, supervised fine-tuning, preference optimization, and model customization strategy.
NEW QUESTION # 21
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation .
Which type of configuration should you use for each requirement? 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:
An Azure AI Hub is the top-level governance container in Microsoft Foundry: it holds shared connections to Azure OpenAI, Azure AI Search, Azure Storage, and other services; it defines network isolation policies; it manages billing and quota at the organizational level. Multiple teams share these resources without each team needing to configure their own connections or negotiate quota independently. An Azure AI Project sits inside the Hub and provides team-level isolation: each project has its own experiments, deployments, prompt flows, evaluations, and fine-tuning jobs, all governed by the Hub ' s shared infrastructure. Different teams get their own project with independent access controls via RBAC, while the platform team manages the shared Hub.
This pattern eliminates redundant resource configurations across teams while maintaining clear team-level boundaries - the correct structure for centralized governance with team isolation.
Microsoft Learn Reference Topic: Microsoft Azure AI Foundry hub and project architecture - Centralized governance and team isolation
NEW QUESTION # 22
A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team's organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?
Answer: C
Explanation:
In this scenario, Data Zone Standard is the most appropriate deployment type for minimizing latency.
While other options cater to high-volume or testing needs, Data Zone Standard is specifically designed for real-time application traffic with a balance of performance and regional availability.
Why Data Zone Standard is the Correct Choice
Real-Time Processing: Unlike the "Batch" options, Data Zone Standard is built for synchronous, real-time requests from internal applications, ensuring the low latency required for interactive user experiences.
Dynamic Routing: It dynamically routes traffic to the most available data centers within a specific Microsoft-defined data zone (e.g., US or EU), which helps maintain responsiveness even if one region experiences high load.
Higher Quotas: It offers higher default throughput (TPM/RPM) than standard regional deployments, allowing multiple business units to access the model simultaneously without hitting restrictive limits that could cause queuing and latency spikes.
Incorrect:
[Not A]
Developer: This deployment type is typically used for initial testing, prototyping, and experimentation rather than high-performance production workloads accessed by many different business units.
[Not B, not D]
Global Batch & Data Zone Batch: These are asynchronous deployment types. They are designed for processing large datasets (like document summaries or mass sentiment analysis) with a 24- hour turnaround time. While they are 50% cheaper, they are not suitable for real-time applications where immediate response is needed.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/deployment-types
NEW QUESTION # 23
......
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