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| Section | Objectives |
|---|---|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Design and implement generative AI solutions | - Large language model integration
|
| Operationalizing machine learning solutions | - Deployment and monitoring
|
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NEW QUESTION # 62
A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?
Answer: A
Explanation:
The best strategy for controlled testing of a new model version in Azure Machine Learning is Blue-Green Deployment, often referred to as a safe rollout.
This approach allows you to deploy a new model version alongside the current one within the same Managed Online Endpoint without disrupting existing users.
Key Features of Blue-Green Deployment in Azure ML
Simultaneous Versions: Both the current "Blue" and new "Green" models run concurrently on the same endpoint.
*-> Traffic Shifting: You can use the endpoint's load balancer to allocate a specific percentage (e.g., 10%) of live production traffic to the new version.
Mirrored Traffic: For even lower risk, you can test the new model with mirrored traffic, where production requests are copied to the new model for validation without using its responses for the end user.
Instant Rollback: If the new model performs poorly, you can instantly shift 100% of traffic back to the original version.
Deployment Headers: You can bypass general traffic splitting to test the "Green" deployment specifically by adding an azureml-model-deployment header to your HTTP requests.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints
NEW QUESTION # 63
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 64
You have an Azure Machine Learning workspace and a data source file ./data/cc_data.csv in the local storage.
You plan to use Azure Machine Learning Python SDK v2 to store the content of the cc.data.csv file in a data asset named cc_data_asset in the workspace.
You write code to connect to the workspace and import all required libraries.
You need to complete the remaining code to ensure it will result in the cc_data_asset that contains the data from cc_data.csv.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 65
You have an Azure Machine Learning workspace.
You plan to set up logging and tracking experiments by using MLflow Tracking.
You need to log the accuracy as a numerical value and the training loss as a plot.
How should you complete the commands? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 66
-
A team is developing a Retrieval-Augmented Generation (RAG) system.
The team requires improvements to the system ' s retrieval quality to ensure accurate, grounded responses.
You need to assess RAG performance before you can suggest an improvement strategy.
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:
Correct sequence:
* Collect retrieval logs.
* Run RAG evaluators.
* Adjust chunking strategy.
* Re-index documents.
The process should begin by collecting retrieval logs . Retrieval diagnostics provide the evidence needed to understand which queries were issued, which document chunks were returned, and whether relevant content is being surfaced. Without retrieval telemetry, optimization becomes speculative rather than measurable.
Next, run RAG evaluators . Microsoft Foundry provides retrieval-oriented evaluators that assess how relevant retrieved context is to a query. The Retrieval evaluator measures contextual relevance without requiring ground truth, while the Document Retrieval evaluator can calculate metrics such as Fidelity and NDCG when labeled retrieval ground truth exists. These measurements help establish the retrieval-quality baseline before modifications are made.
After identifying retrieval deficiencies, adjust the chunking strategy . Microsoft specifically recommends reviewing chunk size and chunking methodology when retrieval returns irrelevant or incomplete passages.
Chunks that are too small can lose context, while oversized chunks can introduce irrelevant material and reduce retrieval precision.
Finally, re-index the documents so the revised chunking configuration is reflected in the searchable corpus.
Changing temperature or regenerating the prompt template primarily affects generation behavior rather than correcting the underlying retrieval pipeline.
Study Guide Reference: Implement generative AI quality assurance and observability - RAG evaluation, retrieval telemetry, retrieval-quality metrics, chunk optimization, indexing, and grounded-response assessment.
NEW QUESTION # 67
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