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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Topic 2: Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Topic 3: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Topic 5: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
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NEW QUESTION # 173
A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose three
Answer: A,C,E
Explanation:
During high traffic, the question is whether the endpoint is keeping up with demand. Requests per minute (B) tells you the actual request load on the endpoint, letting you correlate traffic spikes with degradation.
Connections active (C) reveals whether the endpoint ' s connection pool is saturating - too many concurrent connections without adequate scaling causes queuing and timeouts. Request latency (E) is the gold-standard measure of user-perceived performance; rising latency under load is the clearest signal of degradation. Feature count (A) is a model-design attribute, not a runtime performance metric. Dataset size (D) is a training-time concern unrelated to endpoint performance. Azure Machine Learning online endpoints expose these metrics through Azure Monitor, and Microsoft recommends configuring alert rules on latency and request rate thresholds for all production endpoints.
Microsoft Learn Reference Topic: Monitor Azure Machine Learning online endpoints - Azure Monitor metrics for managed endpoints
NEW QUESTION # 174
Hotspot Question
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 175
Hotspot Question
You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.windows.net/data1.
You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time.
You need to select the parameters of the AzureBlobDatastore class that will point to the target datastore and authorize access to it.
Which parameters should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: container_name = "data1"
Point to target datastore
Example:
# my_blob_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json name: blob_example type: azure_blob description: Datastore pointing to a blob container.
account_name: mytestblobstore
*--> container_name: data-container
credentials:
account_key: XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXX
Box 2: credentials=SASTokenConfiguration
Authorize access
When you create a shared access signature (SAS), the default duration is 48 hours. After 48 hours, you'll need to create a new token.
Consider setting a longer duration period for the time you're using your storage account for Translator Service operations.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/translator/document-translation/how-to-guides/create-sas-tokens
https://www.brainscape.com/flashcards/path2mod1b-make-data-available-creating-13697887/packs/21445573
NEW QUESTION # 176
You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?
Answer: A
NEW QUESTION # 177
You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.
The fine-tuning job uses preference comparison data.
You review the following dataset excerpt.
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:
Preference comparison data with chosen versus rejected response pairs is the input format for Direct Preference Optimization (DPO) or RLHF-style fine-tuning - an advanced fine-tuning technique available in Microsoft Foundry. A valid DPO dataset record must have three fields: a prompt as the input, a chosen field containing the preferred response, and a rejected field containing the less preferred response. The file must be in JSONL format with UTF-8 encoding, where each line represents one complete preference pair. When evaluating statements about this dataset, mark True if the dataset contains all three required fields and chosen responses represent higher-quality outputs than rejected ones. Mark False if the format is incompatible with DPO requirements, if the required rejected field is missing, or if the chosen and rejected responses appear to be of equivalent quality with no clear preference signal.
Microsoft Learn Reference Topic: Advanced fine-tuning with preference data in Microsoft Foundry - DPO dataset format
NEW QUESTION # 178
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