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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
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NEW QUESTION # 37
You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?
Answer: B
Explanation:
Reproducibility requires versioning all components: code, datasets, and environments. Missing any of these elements prevents exact replication of experiments. For example, the same code with different data or dependencies can produce different results, making debugging and auditing difficult.
NEW QUESTION # 38
You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model to an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.
What should the entry script do?
Answer: D
Explanation:
The entry script (scoring script) for an Azure Machine Learning online endpoint must initialize the model when the container starts and process incoming transaction data to return real-time fraud predictions.
init() function: Runs once when the container is initialized. It must locate and load the trained model into memory (typically using a global variable) from the path specified by the AZUREML_MODEL_DIR environment variable.
run(data) function: Executes every time the endpoint receives a real-time HTTP request. It accepts the raw transaction payload, deserializes it, processes the features, passes them to the loaded model for prediction, and returns a JSON-serializable response.
Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
NEW QUESTION # 39
Your ML pipeline contains independent feature engineering steps that currently execute sequentially, increasing overall runtime. You want to optimize execution without modifying logic.
What is the BEST solution?
Answer: C
Explanation:
Parallel execution allows independent pipeline steps to run simultaneously, reducing total execution time without altering logic. This is more efficient than increasing compute resources, which may raise costs without guaranteeing proportional performance improvements.
NEW QUESTION # 40
-
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 # 41
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
Stakeholders must be notified of the drops.
Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
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:
Explanation:
Microsoft ' s guidance on production model monitoring prescribes a four-stage sequence. First, configure an Azure ML model monitor on the deployed endpoint, specifying which metrics to track and on what schedule.
Second, define the threshold value below which model performance is considered unacceptable - this becomes the trigger condition. Third, create an Azure Monitor alert rule that evaluates the monitored metric against the threshold and fires when it is breached; the alert rule is associated with an action group that sends notifications to stakeholders via email, SMS, or Teams webhook. Fourth, the action group includes a webhook action pointing to an Azure Machine Learning pipeline ' s published REST endpoint, which starts the retraining job automatically. This sequence cleanly separates detection, notification, and remediation, matching Microsoft ' s recommended MLOps automation pattern.
Microsoft Learn Reference Topic: Automate model retraining based on monitoring alerts - Azure Machine Learning MLOps
NEW QUESTION # 42
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