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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Topic 2: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 3: Design and implement a GenAIOps infrastructure | 20–25% | - Implement infrastructure for generative AI workloads
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 5: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
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NEW QUESTION # 36
During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?
Answer: C
NEW QUESTION # 37
-
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 # 38
You manage an Azure Machine Learning workspace.
An MLflow model is already registered. You plan to customize how the deployment does inference. You need to deploy the MLflow model to a batch endpoint for batch inferencing. What should you create first?
Answer: B
NEW QUESTION # 39
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?
Answer: C
Explanation:
Azure Machine Learning supports a Table type (mltable). This allows for the creation of a blueprint that defines how to load data files into memory as a Pandas or Spark data frame.
Authoring MLTable Files
To directly create the MLTable file, we recommend that you use the mltable Python SDK to author your MLTable files instead of a text editor.
Supported file types
You can create an MLTable using a range of different file types:
* Delimited Text
(for example, CSV files)
from_delimited_files(paths=[path])
Incorrect:
* Parquet
from_parquet_files(paths=[path])
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable
NEW QUESTION # 40
You manage an Azure Machine Learning workspace.
You must log multiple metrics by using MLflow.
You need to maximize logging performance.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
NEW QUESTION # 41
......
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