AI-300 Practice Test 2 — 10 Questions

Question 1

You need to audit all model access logs across your organization. What is the most effective approach?

Accepted Answer

Enable 'Diagnostic Settings' to send logs to a 'Log Analytics Workspace'.. Enabling diagnostic settings on the Azure AI Services resource to send logs to a Log Analytics workspace is the standard for organization-wide auditing.

Answer

Review individual request headers in the app logs.

Answer

Use 'Azure Advisor' to detect unauthorized access.

Answer

Download the 'Model Usage' report from the billing console.

Question 2

A pipeline step fails because it cannot find a file in the datastore. What is the most likely cause?

Accepted Answer

Incorrect path definition in the component. The mount path or the relative path defined in the pipeline step component configuration is likely incorrect.

Answer

The workspace is too large

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The compute cluster is off

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The subscription is expired

Question 3

You notice that your fine-tuned model is 'forgetting' base capabilities after training on a small dataset. What strategy should you use to mitigate this?

Accepted Answer

Include a portion of the original training data during fine-tuning.. Catastrophic forgetting occurs when a model fine-tuned on a small dataset loses its general capabilities. To mitigate this, you should include a portion of the original training data (or a diverse set of general data) during fine-tuning so the model retains its base knowledge while learning the new task. This is a common technique in continual learning.

Answer

Increase the fine-tuning learning rate.

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Change the model architecture to GPT-4.

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Reduce the batch size to 1.

Question 4

Which parameter in the Azure OpenAI API should be adjusted to make the model's output more deterministic and repeatable?

Accepted Answer

temperature. Temperature controls the randomness of the model's output by scaling the logits before the softmax function. A lower temperature (e.g., 0 or near 0) makes the probability distribution sharper, so the model almost always picks the highest-probability token, resulting in deterministic and repeatable responses. Setting temperature to 0 is the standard way to achieve consistent outputs for the same input in Azure OpenAI.

Answer

max_tokens

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presence_penalty

Answer

frequency_penalty

Question 5

You are deploying a model as a real-time endpoint. You want to ensure the deployment can handle sudden spikes in traffic. Which scaling configuration should you enable?

Accepted Answer

Auto-scaling. Auto-scaling allows the endpoint to dynamically adjust node count based on CPU/Memory usage.

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Static node count

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Batch deployment

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Pre-warmed instances

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None of the above

Question 6

You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?

Accepted Answer

Model prediction precision. Options D and E are correct because retraining triggers based on performance degradation must directly measure how well the model's predictions match ground truth: model prediction precision (D) captures the proportion of positive predictions that are actually correct, and model prediction accuracy (E) captures the overall fraction of correct predictions, both of which decline when the model drifts or degrades. Monitoring these model-quality metrics lets you set thresholds (e.g., accuracy drops below 90% or precision falls by 5%) that signal when retraining is warranted. Option A (CPU usage of the inference cluster) is an infrastructure resource metric that reflects load or scaling needs, not model correctness. Option B (network latency) measures communication delay and says nothing about prediction quality. Option C (number of active users) is a traffic/usage metric that may correlate with load but does not indicate whether the model's predictions have degraded.

Answer

CPU usage of the inference cluster

Answer

Network latency

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Number of active users

Question 7

You want to track your model experiments and compare their performance metrics. Which Azure ML feature provides this capability?

Accepted Answer

Experiments. In Azure Machine Learning, an experiment is the logical container for tracking runs of a model training script. Each run automatically logs metrics, parameters, artifacts, and outputs, enabling you to compare performance across different configurations. This is the core capability for experiment tracking and comparison.

Answer

Environments

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Model Registry

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Compute targets

Question 8

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 applies a penalty proportional to how many times a token has already appeared in the generated text, directly discouraging repeated tokens and reducing redundancy. Its value ranges from -2.0 to 2.0, with positive values increasing the penalty for frequent tokens.

Answer

max_tokens

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presence_penalty

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top_p

Question 9

You are configuring a connection to a vector store. What is the best way to handle the secret credential?

Accepted Answer

Store in Azure Key Vault and reference in the connection.. Key Vault is the centralized store for all application secrets in Azure, which should be linked to the project connection.

Answer

Hardcode the key in the flow configuration.

Answer

Pass it as a parameter in the code.

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Store it in an environment variable.

Question 10

You are building a RAG application and notice that the model sometimes hallucinates information not present in the retrieved documents. Which evaluation metric should you prioritize to mitigate this?

Accepted Answer

Groundedness. Groundedness specifically assesses whether the generated response is derived from the retrieved documents.

Answer

Relevance

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Performance

Answer

Fluency