AI-300 Practice Test 13 — 15 Questions

Question 1

Which Azure feature is used to define infrastructure as code for your Azure ML workspace?

Accepted Answer

Azure Bicep. Azure Bicep is the recommended tool for defining and deploying Azure resources as code.

Answer

Azure Portal

Answer

PowerShell

Answer

Azure CLI

Question 2

Which TWO of the following are benefits of using the Azure ML CLI v2 for your MLOps pipelines?

Accepted Answer

Native integration with CI/CD tools. CLI v2 enables YAML-based infrastructure-as-code and better cross-platform support.

Answer

Automatic model training

Answer

No need for Python

Answer

Built-in model visualization

Question 3

Which TWO of the following are benefits of using Azure ML Environments?

Accepted Answer

Consistent software dependencies. Environments ensure reproducibility and simplify dependency management across environments.

Answer

Network traffic filtering

Answer

Database optimization

Answer

Automatic scaling

Question 4

Which TWO of the following storage types can be registered as an Azure ML Datastore?

Accepted Answer

Azure Blob Storage. Azure Blob Storage and Azure Data Lake Storage Gen2 are both standard datastore types.

Answer

Azure App Service

Answer

Azure Active Directory

Answer

Azure Bot Service

Question 5

You are using GitHub Actions. You want to authenticate to Azure without using hardcoded credentials. What should you use?

Accepted Answer

OIDC. OIDC (OpenID Connect) is the secure way for GitHub Actions to authenticate to Azure.

Answer

SSH keys

Answer

Azure Storage keys

Answer

User name and password

Question 6

Which THREE of the following are types of Azure ML endpoints?

Accepted Answer

Batch endpoint. Managed online, batch, and web service (legacy) represent the main endpoint categories.

Answer

Network endpoint

Answer

Database endpoint

Question 7

Which THREE of the following are common reasons for a model deployment to fail on a Managed Online Endpoint?

Accepted Answer

Missing Python dependencies. Invalid scoring scripts, missing dependencies in the environment, and insufficient resource limits are frequent causes.

Answer

Incorrect storage account name

Answer

Slow internet connection

Question 8

Which THREE steps are necessary to successfully fine-tune an Azure OpenAI model?

Accepted Answer

Prepare and upload a training dataset in JSONL format.. Data preparation, selecting the base model, and managing the fine-tuning job are fundamental steps.

Answer

Manually update the model weights in the code.

Answer

Disable the input filter in the portal.

Question 9

You are debugging a prompt that is performing poorly on edge cases. You decide to use a 'Chain-of-Thought' approach. Why does this improve performance?

Accepted Answer

It enables the model to break down complex logic into sequential steps.. CoT forces the model to generate intermediate reasoning steps, which improves logic in complex tasks.

Answer

It bypasses the safety alignment layer.

Answer

It forces the model to use more input tokens, reducing costs.

Answer

It reduces the context window size.

Question 10

You are optimizing a model for a specific domain language. Which fine-tuning approach minimizes cost while maximizing domain adaptation?

Accepted Answer

Using LoRA (Low-Rank Adaptation).. Low-Rank Adaptation (LoRA) allows for efficient fine-tuning by updating only a small subset of model parameters.

Answer

Training the model from scratch.

Answer

Prompt engineering alone.

Answer

Full parameter fine-tuning.

Question 11

You want to evaluate your prompt engineering changes quantitatively. Which method is most reliable for comparing two prompt versions?

Accepted Answer

Running the prompts against a benchmark evaluation dataset.. A/B testing with a ground-truth dataset allows for objective measurement of performance changes.

Answer

Monitoring the total request count.

Answer

Asking developers for subjective feedback.

Answer

Checking if the model is running on GPT-4.

Question 12

You notice your model is outputting redundant information. Which parameter specifically targets the penalty for repeating tokens?

Accepted Answer

frequency_penalty. The frequency_penalty parameter is explicitly designed to reduce the probability of tokens that have already appeared.

Answer

max_tokens

Answer

presence_penalty

Answer

top_p

Question 13

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.. Re-ranking filters top retrieved results to ensure high-quality context is passed to the LLM.

Answer

Changing the embedding model to a smaller one.

Answer

Increasing the number of chunks retrieved.

Answer

Reducing the temperature to 0.

Question 14

An application is hitting rate limits on the Azure OpenAI service. Which action is the most standard approach for handling this in production?

Accepted Answer

Implement exponential backoff in the client application.. Implementing exponential backoff is the standard architectural pattern for handling rate-limited API responses.

Answer

Delete the deployment and recreate it.

Answer

Disable the rate limit in the portal.

Answer

Increase the temperature of the model.

Question 15

You have a large set of documents for a RAG system. How should you optimize retrieval speed?

Accepted Answer

Store embeddings in a dedicated vector database with indexing.. Using a managed vector store with optimized indexing is essential for large-scale retrieval speed.

Answer

Process all documents in the prompt.

Answer

Increase the model's max_tokens.

Answer

Perform a brute-force search on all documents.