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| Section | Objectives |
|---|---|
| Topic 1: Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Topic 2: Operationalizing machine learning solutions | - Deployment and monitoring
|
| Topic 3: Design and implement generative AI solutions | - Large language model integration
|
| Topic 4: Plan and design AI solutions using Azure AI services | - Responsible AI design
|
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NEW QUESTION # 165
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: A
Explanation:
To ensure an Azure Machine Learning (AML) workspace handling sensitive data is not accessible from the public internet, you must disable the Public Network Access flag and implement Private Endpoints. This configuration creates a private link between your Azure Virtual Network (VNet) and the workspace, ensuring traffic never traverses the public internet.
Reference:
https://www.azadvertizer.net/azpolicyadvertizer/438c38d2-3772-465a-a9cc-7a6666a275ce.html
NEW QUESTION # 166
-
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Data asset type: mltable
Scheme of the URL paths to storage locations: wasbs
The correct data asset type is mltable because the image collection is distributed across multiple storage locations . Microsoft identifies MLTable as the appropriate Azure Machine Learning data asset type when unstructured data such as images, text, or audio is spread across multiple storage locations. An MLTable definition can contain multiple file, folder, or pattern entries, allowing one logical data asset to reference image data residing in different storage accounts.
A uri_folder data asset is appropriate when the required unstructured files reside within a single folder path .
It represents one folder location and therefore does not provide the same multi-path abstraction required by this scenario. uri_file represents only an individual file and is clearly unsuitable for a collection of images.
For the path scheme, Azure Machine Learning uses wasbs:// for Azure Blob Storage. Microsoft documents the canonical format as:
wasbs:// < container > @ < account > .blob.core.windows.net/ < path >
By comparison, abfss:// is used for Azure Data Lake Storage Gen2, while azureml:// identifies Azure Machine Learning datastore paths rather than direct Azure Blob Storage URLs.
Therefore, an MLTable containing multiple wasbs:// paths correctly represents image files distributed across the two Azure Blob Storage accounts.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning data assets, MLTable, multi-location datasets, storage URI schemes, and Blob Storage integration.
NEW QUESTION # 167
Hotspot Question
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
You are reviewing the results of experiment1 by using the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
Filter runs by status
You can also filter experiment by status. It becomes useful to find runs that are running, completed, canceled or failed.
Failed:The job/run has completed with errors.
KILLED: The job/run has been canceled or killed by the user/system.
The following example shows all the completed runs:
runs = mlflow.search_runs(experiment_ids=[ "1234-5678-90AB-CDEFG" ])
runs[runs.status == "FINISHED"]
Box 2: No
Ordering runs
By default, experiments are ordered descending by start_time, which is the time the experiment was queue in Azure Machine Learning.
However, here we have order_by=["start_time ASC"]. The five oldest experiments are returned.
Use the argument max_results from search_runs to limit the number of runs returned.
Box 3: No
Failed: The job/run has completed with errors.
KILLED: The job/run has been canceled or killed by the user/system.
Box 4: No
Getting metrics, parameters, artifacts and models
The method search_runs returns a Pandas Dataframe containing a limited amount of information by default. You can get Python objects if needed, which may be useful to get details about them.
Use the output_format parameter to control how output is returned:
runs = mlflow.search_runs(
experiment_ids=[ "1234-5678-90AB-CDEFG" ],
filter_string="params.num_boost_round='100'",
output_format="list",
)
Details can then be accessed from the info member. The following sample shows how to get the run_id:
last_run = runs[-1]
print("Last run ID:", last_run.info.run_id)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow
NEW QUESTION # 168
Case Study 1 - Fabrikam Inc.
Background
Fabrikam Inc. is a mid-sized healthcare analytics company that provides population health dashboards and predictive insights to regional hospital systems across the United States.
Fabrikam Inc. customers rely on near real time analytics to monitor patient flow, staffing needs, and readmission risks. They use multiple traditional forecasting machine learning models for predictions.
Fabrikam Inc. has an established Microsoft Azure footprint. The company uses Jupyter Notebooks that run on a local server as the primary development environment. The data science team is experiencing scalability, asset management and code management issues with the current development platform. Fabrikam Inc. plans to migrate to a cloud-based development environment to mitigate the issues.
Additionally, the company plans to implement a Retrieval-Augmented Generation (RAG)-based chat application for client support. Leadership requires the application to be developed and deployed with a low operational risk.
Current Environment
Fabrikam Inc. operates a single Azure subscription that has the following components:
* Azure Data Lake Storage Gen2 that contains de-identified clinical and operational datasets
* Azure AI Search indexing curated analytical documents and reference materials
* A small set of Python-based training scripts maintained by data scientists
* Azure OpenAI Service with deployed foundational models
* A Microsoft Foundry resource for building a RAG-based solution
Evaluation data has manually defined expected responses.
The current challenges faced by the data science team include the following:
* Model training jobs are run manually from notebooks.
* Experiment tracking is inconsistent
* Model versions are registered without standardized metadata.
* Deployment is performed manually by data scientists, with limited rollback capability.
* The team has no standardized evaluation process for generative AI outputs.
The environment currently allows public network access. Authentication relies on user accounts rather than managed identities. Compute targets are manually created and shared across experiments. This has led to resource contention during peak usage.
Business Requirements
Fabrikam Inc. has the following business requirements for the modernization initiative:
* Provide a conversational interface that answers analytics questions by using internal documents and datasets.
* Ensure that sensitive healthcare-related data is not exposed outside the Fabrikam Inc. Azure tenant.
* Enable repeatable and auditable model training and deployment processes.
* Support experimentation to compare prompt strategies and fine-tuned models.
* Align the model with the ranked preferences and optimize behavior for the long term.
* Minimize disruption to existing analytics workloads during rollout.
Technical Requirements
To support the business goals, Fabrikam Inc. identifies these technical requirements:
* Use Azure Machine Learning workspaces to centrally manage data assets, models, and environments.
* Implement experiment tracking and model versioning for all training jobs.
* Orchestrate training and evaluation by using pipelines rather than manually running notebooks.
* Deploy traditional machine learning models with support for staged rollout and rollback.
* Improve RAG-based solution output quality.
* Use the existing evaluation datasets that are based on real data with input-output pairs.
* Apply advanced fine-tuning techniques only when prompt engineering is insufficient Issues and Constraints Fabrikam Inc. must comply with internal security policies that require the company to restrict network access and avoid long-lived secrets. The data science team has limited Azure DevOps experience, so solutions must favor managed services and automation over custom infrastructure.
Cost predictability is important. Leadership prefers serverless or managed compute options where possible but is willing to approve dedicated compute for stable production workloads.
Problem Statement
Fabrikam Inc. must design and implement an Azure-based AI operations solution that enables reliable training, evaluation, deployment, and iteration of generative AI models. The solution must support experimentation and gradual rollout while ensuring governance, security, and operational stability. The data science and platform teams must collaborate to deliver this solution by using Azure Machine Learning and Microsoft Foundry capabilities.
Hotspot Question
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:
NEW QUESTION # 169
You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?
Answer: D
Explanation:
The appropriate parameter to use is image.
When defining a custom environment from an existing Docker image using the Environment class in the Azure Machine Learning Python SDK v2, you pass the Docker image registry URI directly to the image parameter.
Python SDK v2 Example
from azure.ai.ml.entities import Environment
# Define the environment using the 'image' parameter
env_docker_image = Environment(
image="pytorch/pytorch:latest", # <--- Appropriate parameter
name="docker-image-example",
description="Environment created from a Docker image."
)
# Register or update the environment in your workspace
ml_client.environments.create_or_update(env_docker_image)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2
NEW QUESTION # 170
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