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Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets |
| 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 generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering - Monitor latency, token usage, cost, and error rates |
| Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
Question #1
You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job.
Which class should you use?
A. command
B. BuildContext
C. EndpointConnection
D. MLClient
Question #2
You are designing an Azure Machine Learning solution for traffic optimization.
The model must be deployed as a web service on a serverless compute and provide real-time predictions based on current traffic and weather conditions. You need to choose an inferencing strategy for the solution.
Which compute should you use?
A. Azure Machine Learning serverless compute
B. Azure Machine Learning batch endpoint
C. Azure Machine Learning online endpoint
D. Azure Machine Learning Kubernetes online endpoints
Question #3
You manage an Azure Machine Learning workspace by using the Azure CLI ml extension v2. You need to define a YAML schema to create a compute cluster. Which schema should you use?
A. https://azuremlschemas.azureedge.net/latest/kubernetesCompute.schema.json
B. https://azuremlschemas.azureedge.net/latest/amlCompute.schemajson
C. https://azuremlschemas.azureedge.net/latest/computdnstarKeichema.json
D. https://azuremlschemas.azureedge.net/latest/vmCompute.schema.json
Question #4
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?
A. Grounded ness
B. Coherence
C. Fluency
D. Textual similarity
Question #5
A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution. (Choose two.)
A. Use the system prompt to establish the role, tone, and style.
B. Repeat the instructions at the end of the system prompt.
C. Increase the temperature setting to encourage creativity.
D. Fine-tune the model for each conversational variation.
E. Apply prompt transformations to separate system instructions from user input.
Solutions:
| Question #1 Correct Answer: A | Question #2 Correct Answer: C | Question #3 Correct Answer: B | Question #4 Correct Answer: C | Question #5 Correct Answer: A,E |



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