Microsoft AI-300 Online Practice Questions and Exam Preparation

Microsoft AI-300 Online Questions & Answers

• Question 51:

  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?

  A. Model
  B. Component
  C. Environment
  D. Pipeline
  A. Model

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  Explanation

  A Machine Learning workspace is the control plane for managing experiment assets, jobs, data, compute, and registered models in Azure Machine Learning. It gives the team a consistent management boundary during experimentation. Other choices such as storage accounts or endpoint resources are narrower and do not satisfy the broader asset-management requirement.

• Question 52:

  A team develops multiple AI applications in Microsoft Foundry that rely on shared prompt templates. The team requires a centralized way to track, version, and reuse prompt content across projects. You need to recommend a solution to track and reuse prompt content.

  Which approach should you recommend?

  A. Store prompts as versioned files in a Git repository.
  B. Register prompts as datasets in the Azure Machine Learning workspace.
  C. Embed prompts directly in application configuration files.
  D. Persist prompts in Azure Blob Storage with folder-level organization.
  A. Store prompts as versioned files in a Git repository.

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  Explanation

  Using a Git repository provides centralized prompt versioning, review, and reuse across projects.

  Registering prompts as datasets, embedding them in app configuration, or using ad hoc blob folders would not give the same source-control workflow. The requirement is prompt content lifecycle management, so Git is the best fit.

• Question 53:

  HOTSPOT

  You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.

  You evaluate the model before and after fine-tuning by using the same evaluation dataset.

  You review the following evaluation results:

  

  You need to determine whether the fine-tuned model shows improved performance without introducing regression.

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  

  

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  The metric table shows groundedness and relevance increasing after fine-tuning, while the hallucination rate decreases rather than regresses. That visual evidence supports improved quality and no hallucination regression. Other interpretations would ignore the direction of the metrics: higher groundedness and relevance are better, and a lower hallucination rate is also better for the requirement.

• Question 54:

  An organization maintains separate Azure Machine Learning workspaces for development and production.

  Both environments must use the same validated assets without duplicating them.

  Assets must be shared across workspaces while maintaining centralized governance and version control.

  You need to enable reuse of assets across workspaces without copying them.

  What should you do?

  A. Enable workspace-level Git integration and sync assets between repositories.
  B. Publish the asset as a pipeline component.
  C. Create a shared Azure Machine Learning environment that includes the asset.
  D. Publish the asset to an Azure Machine Learning registry.
  D. Publish the asset to an Azure Machine Learning registry.

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  Explanation

  An Azure Machine Learning registry is designed for sharing validated assets such as models, components, and environments across workspaces. That avoids duplicating assets between development and production. Other alternatives can store files or run deployments, but they do not provide the governed cross-workspace asset reuse required by the scenario.

• Question 55:

  A data science team creates models in several projects. The team wants a consistent place to manage compute, datastores, environments, and registered assets for a project.

  Which Azure Machine Learning resource should the team use as the primary boundary?

  A. A Machine Learning workspace
  B. A Microsoft Foundry hub
  C. An Azure Storage container
  D. A GitHub repository
  A. A Machine Learning workspace

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  Explanation

  A Machine Learning workspace is the primary boundary for compute, datastores, environments, jobs, and registered assets for a project. A Foundry hub is used for generative AI project organization, storage is only a backing service, and GitHub manages source control. The requirement asks for the Azure Machine Learning project management boundary.

• Question 56:

  An Azure Machine Learning workspace processes sensitive training data. The workspace must NOT be accessible from the public internet.

  You need to restrict network access.

  Which configuration should you implement?

  A. Azure Firewall rules
  B. Private endpoints
  C. Network security groups
  D. Service endpoints
  B. Private endpoints

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  Explanation

  Private networking and disabling or restricting public network access keep an Azure Machine Learning workspace off the public internet. Compute changes, model registration, or application logging do not create the required network boundary. The operational rule is to enforce connectivity at the workspace networking layer when sensitive training data must remain private.

• Question 57:

  DRAG DROP

  A team runs training jobs by using multiple Azure Machine Learning pipelines.

  The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.

  You need to configure the workspace so that runtime dependencies are consistent and reusable.

  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.

  Select and Place:

  

  

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  The diagram answer uses an Azure Machine Learning environment specification to keep packages and system libraries consistent across pipeline runs. Environments make dependencies versioned and reusable, which is what repeatable training jobs require. Other pipeline settings may orchestrate steps, but they do not by themselves lock the Python and operating-system dependencies.

• Question 58:

  You manage a Microsoft Foundry project. You build a multi-turn chatbot application.

  You plan to filter your traces to identify issues while observing how the application is responding.

  The solution must not use an external knowledge base.

  You need to select an evaluation metric.

  Which built-in evaluator should you use?

  A. RelevanceEvaluator
  B. SimilarityEvaluator
  C. QAEvaluator
  D. CoherenceEvaluator
  D. CoherenceEvaluator

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  Explanation

  Filtering traces is part of observability for a multi-turn chatbot because traces expose how requests move through the application and where issues occur. Metrics alone may show that a problem exists, but traces help isolate the behavior.

  Other alternatives such as prompt storage or deployment settings do not provide request-level troubleshooting evidence.

• Question 59:

  A training script logs parameters, metrics, and model artifacts during repeated experiments. The team needs to compare runs and keep an experiment history.

  Which capability should you use?

  A. MLflow experiment tracking
  B. A provisioned throughput unit
  C. A private endpoint
  D. A prompt template stored in Git
  A. MLflow experiment tracking

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  Explanation

  MLflow experiment tracking records parameters, metrics, and artifacts for training runs so the team can compare experiments over time. Provisioned throughput is for generative AI serving capacity, private endpoints are network controls, and Git-managed prompts are prompt lifecycle assets. The requirement is specifically experiment history for model training.

• Question 60:

  HOTSPOT

  A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.

  The model must:

  1. Process text inputs up to 200k tokens long.

  2. Generate concise summaries in seconds.

  3. Support interactive testing before integration into the app.

  You need to select, deploy, and test a model that supports summarization with low latency.

  How should you complete the configuration plan? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

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  The answer selects a text-capable model with a large context window and uses Microsoft Foundry model comparison for validation. Meeting-transcript summarization requires a language model that can process long text, not an audio transcription model. Other testing approaches would not compare candidate deployments against the summarization requirement as directly as Foundry model comparison.