Microsoft AI-300 valid - in .pdf

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  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Q & A: 159 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Q & A: 159 Questions and Answers
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  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Q & A: 159 Questions and Answers
  • Software Price: $59.99
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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Design and implement a GenAIOps infrastructure- Implement RAG (Retrieval-Augmented Generation) pipelines and vector search
- Manage API keys, rate limits, and responsible AI guardrails
- Configure prompt orchestration, prompt flows, and agent frameworks
- Set up Microsoft Foundry and Azure AI services for generative AI workloads
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
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
Implement generative AI quality assurance and observability- Conduct red teaming, adversarial testing, and content filtering
- Evaluate generative AI outputs for quality, safety, and grounding
- Monitor latency, token usage, cost, and error rates
- Implement logging, tracing, and telemetry for GenAI applications
Optimize generative AI systems and model performance- Optimize inference performance, caching, and throughput
- Implement cost management and scaling strategies for GenAI workloads
- Fine-tune and distill models for specific use cases
- Tune prompts, system messages, and grounding strategies

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Locate and capture the model artifacts from the outputs of the training run.
B) Tag the training experiment with a name.
C) Export the model files to local storage.
D) Register the model in the Azure Machine Learning workspace.


2. Hotspot Question
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.

You need to verify whether the training script meets the experiment tracking requirement. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


3. A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?

A) Token limits
B) Temperature parameter
C) Chunk size and overlap
D) Embedding strategy


4. Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.


5. Hotspot Question
A team deploys a generative AI application built by using Microsoft Foundry to production and receives variable traffic throughout the day.
The team requires uninterrupted insight into the application s health and model behavior to detect issues without relying on manual inspection.
You need to select the monitoring capabilities that provide real-time operational visibility into the application.
Which monitoring capability should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Solutions:

Question # 1
Answer: A,D
Question # 2
Answer: Only visible for members
Question # 3
Answer: C
Question # 4
Answer: Only visible for members
Question # 5
Answer: Only visible for members

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