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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| 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 |
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Train, register, and version models using Azure Machine Learning |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You manage an Azure Machine Learning workspace.
You must create and configure a compute cluster for a training job by using Python SDK v2.
You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties.
Which two properties should you define? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Min_instances
- B. size
- C. max_instances
- D. type
- E. name
Correct Answer: A,C 🗳️
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
- A. Azure Kubernetes Service (AKS)
- B. Azure Container Instances (ACI)
- C. Local deployment
- D. Azure Machine Learning compute clusters
Correct Answer: D 🗳️
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A team deploys a generative AI application that uses a model deployed in Microsoft Foundry.
The application must support latency monitoring under production load.
You need to enable performance observability.
Which three 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.
Correct Answer:
Explanation:
Microsoft ' s observability guidance for Azure AI Foundry applications describes a three-stage activation sequence. First, enable Tracing in the Microsoft Foundry project settings before deployment - Tracing instruments the application ' s LLM calls with OpenTelemetry-compatible spans that capture timing data for each step in the flow. Second, deploy the application to a production endpoint so that real traffic flows through the instrumented code path - without actual production traffic, there is no latency data to observe.
Third, configure Azure Monitor and Application Insights to receive, aggregate, and visualize the telemetry emitted by Tracing. Azure Monitor ' s metrics explorer and Application Insights ' performance views display p50, p95, and p99 latency distributions over time, enabling the team to identify latency regressions and set alert thresholds. This sequence - instrument, deploy, visualize - is the canonical Microsoft path to production AI performance observability.
Microsoft Learn Reference Topic: Monitor generative AI applications with Azure Monitor and Microsoft Foundry Tracing
-
You manage a Microsoft Foundry project.
You are evaluating two RAG solutions.
When generating answers, the solutions display the following results:
* The first solution displays low completeness and low utilization.
* The second solution displays low completeness and high utilization.
You need to address the issues found during evaluation.
Which action should you perform first for each issue? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Low completeness, low utilization: Increase chunk size
Low completeness, high utilization: Verify relevant chunks were retrieved For low completeness and low utilization , the retrieved context neither completely answers the query nor is fully used by the model. Microsoft recommends investigating the chunking strategy when completeness is poor. For fixed-size chunking specifically, increasing the chunk size can preserve additional surrounding context and reduce fragmentation of information across separate chunks. This can improve the probability that each retrieved chunk contains enough information to support a complete answer. Decreasing top-k would retrieve fewer chunks and could further reduce completeness, while reducing chunk size can fragment the required context even more.
For low completeness and high utilization , the model is already making effective use of the context supplied to it, but that context does not contain enough information to answer the entire question. Therefore, the first diagnostic step is to verify that all relevant chunks were actually retrieved . Microsoft specifically recommends determining whether additional relevant chunks exist but were not returned and, if so, investigating the retrieval process, ranking strategy, or search configuration.
Prompt tuning is less appropriate as the first step because high utilization indicates that the model is already using the supplied context.
Study Guide Reference: Optimize generative AI systems and model performance - RAG completeness, context utilization, chunking strategy, retrieval evaluation, and search optimization.
-
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Data asset type: mltable
Scheme of the URL paths to storage locations: wasbs
The correct data asset type is mltable because the image collection is distributed across multiple storage locations . Microsoft identifies MLTable as the appropriate Azure Machine Learning data asset type when unstructured data such as images, text, or audio is spread across multiple storage locations. An MLTable definition can contain multiple file, folder, or pattern entries, allowing one logical data asset to reference image data residing in different storage accounts.
A uri_folder data asset is appropriate when the required unstructured files reside within a single folder path .
It represents one folder location and therefore does not provide the same multi-path abstraction required by this scenario. uri_file represents only an individual file and is clearly unsuitable for a collection of images.
For the path scheme, Azure Machine Learning uses wasbs:// for Azure Blob Storage. Microsoft documents the canonical format as:
wasbs:// < container > @ < account > .blob.core.windows.net/ < path >
By comparison, abfss:// is used for Azure Data Lake Storage Gen2, while azureml:// identifies Azure Machine Learning datastore paths rather than direct Azure Blob Storage URLs.
Therefore, an MLTable containing multiple wasbs:// paths correctly represents image files distributed across the two Azure Blob Storage accounts.
Study Guide Reference: Design and implement an MLOps infrastructure - Azure Machine Learning data assets, MLTable, multi-location datasets, storage URI schemes, and Blob Storage integration.
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