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| Section | Objectives |
|---|---|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Design and implement generative AI solutions | - Large language model integration
|
| Plan and design AI solutions using Azure AI services | - Responsible AI design
|
| Implement secure and scalable AI systems | - Scalability and performance optimization
|
>> AI-300 Fragen Und Antworten <<
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128. Frage
Your model requires access to external APIs using sensitive credentials during inference. You must ensure credentials are not exposed in code, logs, or environment variables. What should you implement?
Antwort: A
Begründung:
Azure Key Vault with managed identity ensures secure access to sensitive credentials without exposing them in code or configuration. Managed identities eliminate the need for hardcoded secrets. Other approaches, such as config files or environment variables, increase the risk of accidental exposure.
129. Frage
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
Retrieved results frequently include duplicated content from the same document.
Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Two distinct RAG problems require two distinct solutions. When retrieved results frequently include duplicated content from the same document, the cause is overlapping chunks receiving similarly high similarity scores. The solution is Maximum Marginal Relevance reranking, which diversifies the result set by penalizing results that are semantically similar to already-selected results, reducing redundancy. When retrieved chunks sometimes span unrelated policy sections, the cause is fixed-size character chunking that splits content without regard for semantic boundaries. The solution is semantic chunking - splitting on natural sentence or section boundaries - ensuring each chunk is semantically coherent and does not straddle unrelated content. Both problems must be addressed together: reranking alone solves duplication but not relevance; semantic chunking alone solves relevance but not duplication.
Microsoft Learn Reference Topic: Optimize RAG retrieval in Azure AI Search - Chunking strategies and MMR reranking
130. Frage
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 --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?
Antwort: A
Begründung:
According to Microsoft ' s Azure Machine Learning SDK v2 documentation on command jobs, input arguments in the command string must exactly match the argument names defined in the Python script ' s argparse configuration. The script reads an argument named training_data with an underscore, but the proposed command passes --trainingdata without the underscore. Python ' s argparse treats these as completely different argument names, so the script would receive None for its --training_data argument, causing a runtime error. The placeholder syntax for referencing the input in the command string is correct, which is a point in this solution ' s favor. However, the argument name mismatch alone is sufficient to make this solution fail. The corrected command would use --training_data exactly matching the argument name defined in the script.
Microsoft Learn Reference Topic: Configure command job inputs and outputs - Azure Machine Learning SDK v2 reference
131. Frage
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 in 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: Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
Does the solution meet the goal?
Antwort: A
Begründung:
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Configure Azure Monitor to collect logs from the workspace. Use the logs to perform prompt evaluation.
* 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
132. Frage
You manage an Azure Machine Learning workspace.
You must set up an event-driven process to trigger a retraining pipeline.
You need to configure an Azure service that will trigger a retraining pipeline in response to data drift in Azure Machine Learning datasets. Which Azure service should you use?
Antwort: B
133. Frage
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