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| Section | Objectives |
|---|---|
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows - Implement security, governance, and compliance for MLOps - Manage environments, data stores, and model registries |
| Optimize generative AI systems and model performance | - 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 - Optimize inference performance, caching, and throughput |
| Implement generative AI quality assurance and observability | - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding |
| Design and implement a GenAIOps infrastructure | - 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 - Configure prompt orchestration, prompt flows, and agent frameworks |
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NEW QUESTION # 151
You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?
Answer: B
Explanation:
To fine-tune a model in the Azure OpenAI Service, your training data must be in JSONL (JSON Lines) format.
File Requirements
Format: Each line of the file must be a single, valid JSON object.
Encoding: The file must be encoded in UTF-8.
Special Requirement: For some older tools or specific Azure OpenAI use cases, the file may need to include a byte-order mark (BOM).
Size Limits: Individual files typically have a maximum size (e.g., 100 MB per file in some contexts), but you can often upload multiple files.
Reference:
https://dev.to/icebeam7/fine-tuning-a-model-with-azure-open-ai-studio-39p7
NEW QUESTION # 152
Your ML pipeline contains independent feature engineering steps that currently execute sequentially, increasing overall runtime. You want to optimize execution without modifying logic.
What is the BEST solution?
Answer: D
Explanation:
Parallel execution allows independent pipeline steps to run simultaneously, reducing total execution time without altering logic. This is more efficient than increasing compute resources, which may raise costs without guaranteeing proportional performance improvements.
NEW QUESTION # 153
-
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Load the model when the service starts: init()
Use the model to score new data: run()
Azure Machine Learning scoring scripts for online endpoints use two required entry-point functions: init() and run() . Microsoft explicitly states that the scoring script specified for an online deployment must contain both functions.
The init() function is invoked when the inference container is initialized or started, typically immediately after a deployment is created or updated. It is intended for one-time initialization tasks such as locating the registered model through AZUREML_MODEL_DIR, deserializing the model, and storing it in memory.
Loading the model once during initialization avoids repeatedly loading it for every inference request, which reduces latency and processing overhead.
The run() function is called each time the endpoint receives an inference request. It accepts the incoming request data, transforms or parses the input as required, invokes the loaded model ' s prediction logic, and returns the scoring result. Microsoft describes run() as the function that performs the actual scoring or prediction for each endpoint invocation.
main(), score(), and predict() may exist inside application code or model libraries, but they are not the required Azure Machine Learning scoring-script entry points.
NEW QUESTION # 154
A team is experimenting with traditional models for a classification workflow in Azure Machine Learning.
The team requires a consistent way to manage assets that are created during experimentation.
You need to ensure that artifacts can be reused and governed across projects.
Which asset should you register?
Answer: A
Explanation:
During experimentation, a data science team produces trained model artifacts they want to share with the MLOps team for deployment, with the governance team for compliance, and with the QA team for testing.
Registering the model in the Azure ML model registry gives each of these teams access to the versioned, immutable artifact through a single reference. Option B (Component) packages a reusable pipeline step, not the model output itself. Option C (Environment) captures the Python runtime, not the trained artifact. Option D (Pipeline) defines the orchestration workflow, not the resulting model. In Azure ML, the model registry is the governance store for trained model artifacts, recording who created it, when, from what data and code, and which metrics it achieved - making it the asset to register for reuse and governance across projects.
Microsoft Learn Reference Topic: Register and manage models in Azure Machine Learning - Model registry
NEW QUESTION # 155
You manage an Azure Machine Learning workspace That has an Azure Machine Learning datastore.
Data must be loaded from the following sources:
* a credential-less Azure Blob Storage
* an Azure Data Lake Storage (ADLS) Gen 2 which is not a credential-less datastore You need to define the authentication mechanisms to access data in the Azure Machine Learning datastore.
Which data access mechanism should you use? To answer, move the appropriate data access mechanisms to the correct storage types. You may use each data access mechanism once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 156
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