AI-300 Practice Test 6 — 10 Questions

Question 1

You are deploying a model via a Managed Online Endpoint. You want to implement a Canary deployment where 10% of traffic is sent to a new model version. How do you configure this?

Accepted Answer

Configure the 'traffic' parameter in the 'az ml online-endpoint update' command.. You use the 'update' command on the endpoint to set the 'traffic' parameter, distributing weights across deployment names.

Answer

Modify the 'inference_config.json' file to include weights.

Answer

Create two endpoints and use an Azure Load Balancer.

Answer

Set the 'replica_count' to 10% of total capacity.

Question 2

You have an automated model training pipeline that is failing intermittently due to compute availability. What should you configure to ensure the pipeline is more resilient?

Accepted Answer

Configure 'retry' settings for the step.. You should configure 'retry' settings in the 'PipelineStep' definition to handle transient compute errors.

Answer

Increase the 'max_nodes' of the cluster.

Answer

Set the 'allow_reuse' parameter to True.

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Use 'LowPriority' compute.

Question 3

What is the recommended approach for managing configuration settings for different environments (Dev, Test, Prod) in an AI application?

Accepted Answer

Use 'App Configuration' service to manage environment-specific variables.. Using environment variables or app configuration services is the standard way to inject configuration without hardcoding.

Answer

Deploy different model versions for each environment.

Answer

Create separate Azure subscriptions for every environment.

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Hardcode values in the application code.

Question 4

Which technique is most appropriate for optimizing RAG performance when the vector database returns too much noisy information?

Accepted Answer

Implementing a re-ranking stage.. A re-ranking stage applies a more expensive cross-encoder or semantic ranker to the initial candidate set returned by the vector database, reordering results by true relevance to the query. This filters out semantically similar but contextually irrelevant chunks that vector similarity alone cannot distinguish, directly addressing the noisy-retrieval problem. It is the standard second-stage pattern in production RAG pipelines.

Answer

Changing the embedding model to a smaller one.

Answer

Increasing the number of chunks retrieved.

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Reducing the temperature to 0.

Question 5

You are using Azure AI Studio to evaluate your model. Which tool allows you to perform batch testing on a large dataset of prompts?

Accepted Answer

Evaluation tab. The Evaluation tab in Azure AI Studio (now Azure AI Foundry) provides batch evaluation capabilities, allowing you to run a model or prompt flow against a large dataset of prompts and compute metrics like groundedness, relevance, and fluency. This is the designated tool for batch testing at scale, as opposed to interactive testing in the playground.

Answer

Prompt Playground

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Model catalog

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Deployment logs

Question 6

You are implementing a custom container for model training. You need to push the image to the 'Azure Container Registry' (ACR) linked to your workspace. What is the correct command?

Accepted Answer

'docker build' and 'docker push' to the ACR URL.. The standard approach is 'docker build' followed by 'docker push' to the ACR URL, ensuring authentication via 'az acr login'.

Answer

'az ml environment create --custom'.

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'az container create'.

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'az ml model deploy --image'.

Question 7

You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?

Accepted Answer

Azure ML Components. Azure ML components allow for independent versioning and reusability across different pipelines.

Answer

Azure ML Datasets

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Azure ML Pipelines Steps

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Azure Container Registry images

Question 8

Which TWO of the following are benefits of using Prompt Flow for orchestration?

Accepted Answer

Integrated testing and evaluation.. Option B is correct because Prompt Flow provides built-in tooling for testing and evaluating flows, including the ability to run batch evaluations against datasets and compute metrics such as groundedness, relevance, and coherence, which supports iterative quality improvement. Option E is correct because Prompt Flow offers a visual designer (in Azure AI Foundry / Azure Machine Learning) where you can author and connect nodes—LLM, Python, prompt, and tool nodes—into directed acyclic graphs, making complex orchestration easier to design and understand. The unmarked options do not belong: A is wrong because Prompt Flow orchestrates calls to LLMs rather than removing the need for one, C is wrong because authentication and authorization are still required for accessing models and connected resources, and D is wrong because Prompt Flow does not automatically manage hardware—compute provisioning and scaling remain the user's responsibility.

Answer

Removal of the need for an LLM.

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No need for authentication.

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Automatic hardware management.

Question 9

What is the benefit of using 'Azure Machine Learning Datasets' (or Data Assets) over raw storage paths?

Accepted Answer

They provide versioning and lineage tracking.. Data Assets allow for versioning, lineage tracking, and simplified usage across different compute targets.

Answer

They convert data to Parquet format.

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They automatically remove duplicate records.

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They are faster to read.

Question 10

What is the primary benefit of using Azure ML compute clusters instead of compute instances for training?

Accepted Answer

Support for multiple nodes. Compute clusters are designed for distributed, scalable training across multiple nodes.

Answer

Clusters have more RAM per node

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Instances are deprecated

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Cost is always lower