AI-300 無料問題集「Microsoft Operationalizing Machine Learning and Generative AI Solutions」
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
正解：D
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Hotspot Question
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.
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.
正解：
Explanation:
Box 1: uri_folder
The best data asset type for this scenario is a File data asset (uri_file or uri_folder).
In Azure Machine Learning, image files used for training computer vision models are best managed as file-based data assets because machine learning frameworks read individual images directly from storage paths rather than tabular rows.
Preserves Formats: Keeps images in their native formats (PNG, JPEG).Direct Access: Allows training scripts to mount or download files easily.
*-> Folder Mapping: A uri_folder references the entire directory containing your images.
Efficiency: Avoids the overhead of parsing unstructured binary data into a table.
Box 2: azureml
To point a uri_folder data asset to your Azure Blob Storage locations in Azure Machine Learning, you should use the azureml:// URI scheme.
This is the recommended, modern standard that leverages Azure ML datastores for secure tokenless access.
Alternatively, you can use direct Azure Storage URI schemes depending on your configuration Recommended Scheme: Azure ML Datastore This scheme abstracts the storage credentials by referencing an Azure ML Datastore.
Format: azureml://datastores/<datastore_name>/paths/<path_to_folder>/
Example: azureml://datastores/myblobdatastore/paths/images/training_set/ Incorrect:
[not wasbs]
Azure Blob Storage (wasbs)
Used when connecting via the legacy Windows Azure Storage Blob driver.
Format:
wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_folder>/
[Not abfss]
Azure Data Lake Storage Gen2 (abfss)
Used if your Blob Storage accounts have the hierarchical namespace enabled.
Format:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path_to_folder>/ Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets
An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?
正解：C
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Hotspot Question
You review the following Azure CLI command and the relevant Bicep excerpt.
(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
You review the following Azure CLI command and the relevant Bicep excerpt.
(Non-relevant sections are omitted.)
You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
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.
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.
正解：
Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create an environment.
Does the solution meet the goal?
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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create an environment.
Does the solution meet the goal?
正解：A
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解説: (JPNTest メンバーにのみ表示されます)
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file.
The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?
The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?
正解：A
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解説: (JPNTest メンバーにのみ表示されます)
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 on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?
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 on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?
正解：A
解答を投票する
解説: (JPNTest メンバーにのみ表示されます)
You use Azure Machine Learning to train a model.
You must use Bayesian sampling to tune hyperparameters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
You must use Bayesian sampling to tune hyperparameters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
正解：C、D
解答を投票する
解説: (JPNTest メンバーにのみ表示されます)
Hotspot Question
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
Explanation:
Box 1: Real-time tracing
Observe live request flow.
To best observe the live request flow in your generative AI application, you should use Real-time tracing.
Real-time tracing maps the end-to-end journey of a request as it flows through various microservices, APIs, and AI model endpoints. It provides the exact visibility needed to pinpoint bottlenecks, latencies, and failures in the live request path immediately.
Box 2: Aggregated metrics
Identify recurring failure patterns.
To best identify recurring failure patterns in a generative AI application with variable traffic, you should use aggregated metrics.
Aggregated metrics groups data points over specified time intervals to reveal long-term trends, error rates, and system anomalies. It allows you to track patterns (like a spike in 5xx errors or model timeouts during peak traffic) over hours or days without manually inspecting individual logs.
Reference:
https://docs.azure.cn/en-us/ai-services/metrics-advisor/glossary