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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Topic 2: Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Topic 3: Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Topic 4: Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Topic 5: Implement machine learning model lifecycle and operations | 25–30% | - Deploy models to production
|
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NEW QUESTION # 135
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?
Answer: C
Explanation:
To use a new version of the Docker image for an environment using the Azure Machine Learning SDK v2, you must instantiate an Environment class object with the new image parameter and then use the ml_client.environments.create_or_update() method.
Required Steps
1. Define the updated Environment: Use the Environment entity from the azure.ai.ml.entities package. Set the image parameter to the URI of the new Docker image version.
2. Register or update the asset: Pass the environment instance into
ml_client.environments.create_or_update() to create a new version of that environment asset within your workspace.
Reference:
https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment-with-private- packages/environment-with-private-package-docker-image.ipynb
NEW QUESTION # 136
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 a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
The application retrieves documents from an indexed knowledge base and generates answers for internal users.
Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
Solution: Review user feedback comments collected after deployment to determine whether answers appear more accurate.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* Review user feedback comments collected after deployment to determine whether answers appear more accurate.
Relying solely on user feedback comments is not the proper immediate action for evaluating this specific issue but it is still the best choice.
Evaluate whether your proposed change improves the Retrieval-Augmented Generation (RAG) system by reducing hallucinations (unsupported information), you need to measure faithfulness and context relevance using automated, quantifiable metrics.
The proper course of action is to implement an LLM-as-a-Judge framework using an open-source evaluation library like Ragas or TruLens.
Recommended Evaluation Plan
Establish a baseline: Run your current RAG pipeline through a test dataset of 50-100 representative user queries.
Capture the outputs: Save the user query, the exact retrieved document snippets, and the generated response for every test.
Apply the change: Deploy your proposed modification (e.g., altered prompt, different temperature, or re-ranking algorithm).Run the evaluation: Pass the test dataset through the updated pipeline to generate a new set of responses.
Compare the metrics: Use the framework to score both sets of data and mathematically verify if the change reduced unsupported claims.
Incorrect:
* Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Comparing embedding vector dimensions is not a valid or effective method for measuring RAG answer quality. Vector dimensions (e.g., 1536 or 3072) are static architectural properties of your embedding model. They do not reflect factual accuracy, semantic grounding, or the rate of hallucinations in your text generation.
* Measure token throughput and average response latency before and after applying the proposed change.
Measuring token throughput and latency is not the correct action to solve this specific problem.
Reference:
https://www.getmaxim.ai/articles/how-to-evaluate-your-rag-system/
NEW QUESTION # 137
Hotspot Question
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.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
Filter runs by status
You can also filter experiment by status. It becomes useful to find runs that are running, completed, canceled or failed.
Failed:The job/run has completed with errors.
KILLED: The job/run has been canceled or killed by the user/system.
The following example shows all the completed runs:
runs = mlflow.search_runs(experiment_ids=[ "1234-5678-90AB-CDEFG" ])
runs[runs.status == "FINISHED"]
Box 2: No
Ordering runs
By default, experiments are ordered descending by start_time, which is the time the experiment was queue in Azure Machine Learning.
However, here we have order_by=["start_time ASC"]. The five oldest experiments are returned.
Use the argument max_results from search_runs to limit the number of runs returned.
Box 3: No
Failed: The job/run has completed with errors.
KILLED: The job/run has been canceled or killed by the user/system.
Box 4: No
Getting metrics, parameters, artifacts and models
The method search_runs returns a Pandas Dataframe containing a limited amount of information by default. You can get Python objects if needed, which may be useful to get details about them.
Use the output_format parameter to control how output is returned:
runs = mlflow.search_runs(
experiment_ids=[ "1234-5678-90AB-CDEFG" ],
filter_string="params.num_boost_round='100'",
output_format="list",
)
Details can then be accessed from the info member. The following sample shows how to get the run_id:
last_run = runs[-1]
print("Last run ID:", last_run.info.run_id)
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow
NEW QUESTION # 138
You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
Answer: D
Explanation:
In Microsoft Foundry, when configuring and running a fine-tuning job for analyzing customer feedback (e.g., classifying support tickets), you should first enable tracing for all inference calls in the evaluation pipeline.
Tracing is a critical step in the "Evaluate" phase of the fine-tuning workflow, allowing you to capture input/output examples, identify the root cause of classification errors, monitor latency, and analyze model behavior before and after training.
Note:
To fine-tune a model and evaluate it effectively, you should follow this sequence:
1. Enable Tracing for Initial Baseline
Before you fine-tune, you should indeed enable tracing for all inference calls in your existing evaluation pipeline.
Purpose: This creates a clear "paper trail" of how the base model is currently failing.
Benefit: Tracing captures the exact inputs, outputs, and intermediate steps. By analyzing these traces, you can identify if the low accuracy is due to the model not following instructions, missing domain-specific jargon, or struggling with specific ticket categories.
Setup: You can enable Automatic Tracing (currently in preview) in Foundry to log these details to Application Insights without changing your code.
2. Configure and Run Fine-Tuning
Once you have analyzed the traces and prepared your labeled dataset, you can proceed with the fine-tuning job.
3. Continuous Evaluation
After the job completes, you must compare the fine-tuned model against your original traces Reference:
https://devblogs.microsoft.com/foundry/a-developers-guide-to-fine-tuning-gpt-4o-for-image- classification-on-azure-ai-foundry
NEW QUESTION # 139
You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?
Answer: C
Explanation:
Batch endpoints are optimized for large-scale, asynchronous inference workloads. They efficiently process large datasets and scale based on demand, making them cost-effective for non-real-time scenarios. Online endpoints are designed for low-latency use cases and are more expensive for batch processing.
NEW QUESTION # 140
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