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| Section | Objectives |
|---|---|
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps |
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NEW QUESTION # 116
You need to configure an optimization method to meet Fabrikam Inc.'s technical requirements.
Which strategy should you apply first? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Domain specialization: Supervised fine-tuning
Poor response accuracy of a RAG-based solution: Apply prompt engineering For domain specialization , Supervised Fine-Tuning (SFT) is the correct first strategy. Microsoft describes SFT as the foundational fine-tuning technique for training a model from labeled input-output pairs , and specifically identifies domain specialization as one of its principal use cases. Microsoft also recommends starting with SFT for most customization projects because it supports task specialization, instruction following, style, and domain-specific behavior. Fabrikam already possesses evaluation data containing input- output pairs, which aligns directly with the SFT data model.
For poor RAG response accuracy , the first action is prompt engineering . The case explicitly requires advanced fine-tuning only when prompt engineering is insufficient. In a RAG system, prompt engineering determines how the model interprets retrieved context, constrains answers to grounding information, handles missing evidence, and formats responses. Microsoft notes that inadequate RAG prompting can produce false or incomplete answers even when retrieval returns appropriate content.
DPO is primarily appropriate for alignment using preferred versus non-preferred responses, while RFT targets complex reward-based reasoning optimization. Neither is the initial technique for domain specialization in this requirement.
Study Guide Reference: Optimize generative AI systems and model performance - prompt engineering, RAG optimization, supervised fine-tuning, preference optimization, and model customization strategy.
NEW QUESTION # 117
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
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:
NEW QUESTION # 118
Hotspot Question
A team is standardizing MLOps practices by using automated deployments.
The team requires infrastructure to be defined declaratively and deployed through automation pipelines.
You need to configure infrastructure deployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Model Registry
Deploy resources from a pipeline.
To standardize MLOps practices with declarative infrastructure and automated pipelines, the best choice is to use a Model Registry via Infrastructure as Code (IaC) for your core setup, supplemented by Azure CLI inside your pipelines for operational tasks. Prompt Flow is an application development tool and should not be used to deploy infrastructure.
A Model Registry (like Azure Machine Learning registry or MLflow) is the standard architectural pattern for managing ML artifacts declaratively.
Declarative Tracking: It stores model versions, lineages, and environments as code-like configurations.
Pipeline Integration: CI/CD pipelines can natively fetch from or push to a registry using configuration files.
Environment Isolation: It allows you to promote the exact same model artifact across Dev, Staging, and Production environments without rebuilding.
Box 2: Bicep templates
Define Azure resources declaratively.
Bicep templates are an excellent choice for defining Azure resources declaratively in an automated MLOps pipeline.
Native Azure Integration: Bicep supports all Azure Machine Learning (Azure ML) resources immediately upon release.
Declarative Syntax: You define the desired end-state of your infrastructure without writing complex deployment scripts.
No State Management: Azure manages the state automatically, unlike Terraform which requires a remote state file.
Tooling Support: Deep integration with Azure Pipelines and GitHub Actions allows easy deployment validation.
Reference:
https://azure.microsoft.com/en-us/blog/new-azure-capabilities-to-simplify-deployment-and-management/
NEW QUESTION # 119
-
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 # 120
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: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Does the solution meet the goal?
Answer: B
Explanation:
Navigate to the Microsoft Foundry project in the Azure portal or Foundry portal and open the prompt flow for which you want to capture telemetry. Locate the Tracing toggle in the project or flow settings and enable it - this single action instructs the Foundry runtime to instrument every LLM call within the flow. Execute test runs of the prompt flow through the portal ' s test interface or via the CLI. Open the Traces view in Microsoft Foundry: each run appears as a trace with a tree of spans, where each span corresponds to one LLM call or tool invocation. Every span captures the exact input sent to the model, the model ' s output, token counts including prompt tokens and completion tokens, and wall-clock latency. Compare runs across prompt variants by examining individual traces side-by-side. This solution directly satisfies all four capture requirements.
Microsoft Learn Reference Topic: Enable and use tracing in Microsoft Foundry - Capturing LLM call telemetry for evaluation
NEW QUESTION # 121
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