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| Section | Objectives |
|---|---|
| Topic 1: Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 2: Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications |
| Topic 3: Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search |
| Topic 4: Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads |
| Topic 5: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
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NEW QUESTION # 13
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 work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Create prompt variants and compare their outputs in the Evaluation experience.
Does the solution meet the goal?
Answer: B
Explanation:
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Create prompt variants and compare their outputs in the Evaluation experience.
* Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Note:
In Azure AI Foundry, you can capture and compare these metrics by enabling Tracing and using the Bulk Test feature. This allows you to systematically evaluate different prompt variants against a common dataset.
Steps to Evaluate and Compare Prompt Variants
*-> 1. Enable Tracing
Navigate to your Prompt Flow project.
Locate the Tracing toggle at the top of the flow authoring page.
Switch it to On.
This ensures every execution captures latency, token counts, and node-level inputs/outputs.
2. Create Prompt Variants
Within your flow, identify the LLM node you want to test.
Click Variants to create multiple versions of your prompt (e.g., Variant_0, Variant_1).
This allows you to test different instructions or few-shot examples side-by-side.
3. Run a Bulk Test (Evaluation)
4. Analyze the Results
Reference:
https://www.linkedin.com/pulse/streamlining-generative-ai-development-azure-foundry-tracing- taneja-mbwze
NEW QUESTION # 14
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 # 15
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 an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.
The training_data argument specifies the path to the training data in a file named dataset 1. csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset 1. csv
Does the solution meet the goal?
Answer: B
Explanation:
This solution attempts to pass the dataset filename as a positional argument, bypassing the entire Azure Machine Learning input-binding mechanism. In Azure ML command jobs, data assets are not simple local file paths - they are managed references to data stored in Azure Storage that Azure ML resolves and mounts or downloads to the compute target at runtime. The script cannot directly reference dataset1.csv as a bare filename because there is no guarantee that file will be in the working directory of the compute target.
Moreover, the script ' s argparse configuration uses --training_data as a named parameter, not a positional argument, so the command syntax does not match the expected interface. The correct approach uses the input binding placeholder syntax, which instructs Azure ML to resolve the registered data asset and provide its local path to the script at runtime.
Microsoft Learn Reference Topic: Work with data in Azure Machine Learning command jobs - Input binding syntax
NEW QUESTION # 16
You manage an Azure Machine learning workspace named workspace1.
You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class.
You need to add the instantiated compute instance to workspace 1.
What should you use?
Answer: C
NEW QUESTION # 17
Drag and Drop Question
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:
NEW QUESTION # 18
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