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| Section | Objectives |
|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails |
| Topic 2: Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health |
| Topic 3: Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates |
| Topic 4: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies |
| Topic 5: Design and implement an MLOps infrastructure | - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries |
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NEW QUESTION # 41
A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?
Answer: B
Explanation:
The correct approach is to create an Azure Machine Learning sweep job that runs multiple training trials with different hyperparameter combinations. Microsoft documents sweep jobs as the standard SDK v2 mechanism for automated hyperparameter tuning. A sweep job defines a search space for the tunable parameters, selects a sampling algorithm such as random, grid, or Bayesian sampling, identifies a primary optimization metric, and executes multiple child trials automatically.
Each trial receives a different combination of values sampled from the configured search space. This allows the same underlying training command or script to be reused while Azure Machine Learning supplies different hyperparameter values for each run. The number of trials and the degree of parallelism can also be controlled through settings such as max_total_trials and max_concurrent_trials.
Option A performs no tuning because only one fixed configuration is evaluated. Option B changes parameters too late because hyperparameter tuning belongs to the training and validation lifecycle, not post-deployment operation. Option C relies on defaults and does not explore alternative configurations.
Therefore, the required solution is D: Create a tuning job that runs multiple trials with different parameter values.
Study Guide Reference: Implement machine learning model lifecycle and operations - hyperparameter tuning, sweep jobs, search spaces, sampling algorithms, trial execution, and experiment optimization.
NEW QUESTION # 42
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images and retrain the model.
You need to use the Azure Machine Learning Python SEX v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties with frequency set to month ' and interval set to " 1:
Which three classes should you instantiate 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:
NEW QUESTION # 43
You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?
Answer: A
Explanation:
When labeled data is unavailable, traditional accuracy metrics cannot be computed. Data drift detection monitors changes in input data distribution, serving as a proxy for potential performance degradation. This allows early detection of issues before labeled data becomes available.
NEW QUESTION # 44
A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
To improve Retrieval-Augmented Generation (RAG) accuracy, address inconsistent retrieval, and eliminate incomplete answers without changing the embedding model or increasing costs significantly, you must move beyond naive fixed-length chunking and implement a two-stage retrieval process.
Here is the targeted, low-cost strategy:
1. Tune Chunk Size and Overlap to Match Content Structure
Inconsistent retrieval often occurs because important information is split across chunk boundaries (breaking context) or chunks are too large, diluting the semantic meaning.
2. Implement an Optimized Re-ranker
The initial vector search often returns "noise"-chunks that are semantically close but not actually relevant. A re-ranker acts as a second, smarter, but more "expensive" step that works on a smaller subset of data, making it low-cost overall.
Reference:
https://medium.com/@sthanikamsanthosh1994/how-to-improve-rag-retrieval-augmented- generation-performance-2a42303117f8
NEW QUESTION # 45
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
Answer: A
Explanation:
In Microsoft Foundry, you can test and compare LLM prompt variants using Prompt Flow. This environment allows you to create a controlled development workflow with consistent inputs, bypassing the need for live user traffic.
1. Generate Synthetic Interaction Data
Generating synthetic data is the recommended first step to build a robust, diverse test dataset without manual effort.
2. Compare Prompt Variants
Once your synthetic dataset is ready, use Prompt Flow to iterate on your prompts.
3. Controlled Evaluation
To measure performance objectively, run automated Eval Runs.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/concepts/prompt-flow
NEW QUESTION # 46
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