AIF-C01 Practice Test 6 — 10 Questions

Question 1

A company is using a foundation model on Amazon Bedrock to generate customer support responses. They notice that the model sometimes produces harmful or offensive content. Which approach is MOST effective to mitigate this issue?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Configure Amazon Bedrock Guardrails with content filters. Amazon Bedrock Guardrails provides configurable content filters that can block harmful, offensive, or inappropriate content in both user inputs and model outputs. This is the most effective approach because it operates at the inference layer, applying safety policies consistently across all requests without requiring model retraining or manual review. Prompt engineering alone is unreliable, and fine-tuning may not generalize to all harmful content patterns.

Answer

Use prompt engineering to instruct the model to avoid harmful content

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Enable model invocation logging to review and block responses

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Fine-tune the model on a curated dataset of safe responses

Question 2

A data scientist is fine-tuning a foundation model on SageMaker. They want to prevent overfitting. Which THREE actions can help? (Select THREE.)

Accepted Answer

Apply dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. In SageMaker, dropout can be applied via framework-specific APIs (e.g., `tf.keras.layers.Dropout` in TensorFlow) or by configuring the model architecture in the training script.

Answer

Increase the number of epochs

Answer

Use a smaller learning rate

Question 3

A data governance team wants to enforce fine-grained access control on data in an Amazon S3 data lake used by multiple business units for AI training. Which AWS service should they use to define and manage data permissions at the table and column level?

Accepted Answer

AWS Lake Formation. AWS Lake Formation provides fine-grained access control for data lakes, allowing you to set permissions on tables and columns across AWS analytics and ML services.

Answer

AWS Identity and Access Management (IAM)

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AWS CloudTrail

Answer

Amazon Macie

Question 4

Refer to the exhibit. A team is configuring a SageMaker Model Bias job. The baseline job has been completed. However, the bias job fails with a resource not found error. What is the most likely cause?

Accepted Answer

The BaseliningJobName is incorrect. The bias job requires a reference to the completed baseline job to compare the training data against. If the BaseliningJobName parameter is incorrect or does not match the actual name of the completed baseline job, SageMaker will throw a 'ResourceNotFound' error because it cannot locate the specified baseline job. The error is not related to timeouts, instance types, or IAM permissions for describing the baseline job.

Answer

The StoppingCondition is too short

Answer

The instance type ml.m5.large is not supported

Answer

The IAM role lacks permissions to DescribeBaselineJob

Question 5

A developer is building an application that translates customer support tickets from English to Spanish using Amazon Bedrock. They need to evaluate the quality of translations. Which automated metric is most appropriate for comparing the model's translations to professional human translations?

Accepted Answer

BLEU. BLEU is a standard metric for evaluating machine translation quality by comparing n-gram overlap with reference translations.

Answer

Accuracy

Answer

BERTScore

Answer

ROUGE

Question 6

An organization wants to prototype a new generative AI application and allow multiple team members to collaborate on prompt engineering and model selection without writing code. Which tool should they use?

Accepted Answer

Amazon Bedrock Studio. Amazon Bedrock Studio is a web-based collaborative environment for prompt engineering, model experimentation, and building generative AI applications without coding. Bedrock Playground is for single-user testing. SageMaker Canvas and CodeWhisperer serve different purposes.

Answer

Amazon SageMaker Canvas

Answer

Amazon Bedrock Playground

Answer

Amazon CodeWhisperer

Question 7

A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 records with 9,500 non-churners and 500 churners. After training a logistic regression model, the model achieves 95% accuracy on the test set. However, the business team reports that the model is not useful because it predicts almost all customers as non-churners. Which metric should the data scientist use to evaluate the model's performance in this scenario?

Accepted Answer

Recall. (Recall) is correct because in this highly imbalanced dataset (95% non-churners vs 5% churners), the model's 95% accuracy is misleading—it can achieve this by simply predicting the majority class (non-churner) for all samples. Recall measures the proportion of actual churners correctly identified (True Positives / (True Positives + False Negatives)), directly addressing the business need to detect churn. A high recall ensures the model captures most churners, even at the cost of some false positives.

Answer

Accuracy

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R-squared

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Precision

Question 8

Which TWO practices help ensure transparency in AI systems? (Choose 2)

Accepted Answer

Use model-agnostic explainability tools like SHAP. Model-agnostic explainability tools like SHAP (SHapley Additive exPlanations) provide post-hoc explanations for any machine learning model by computing feature contributions based on cooperative game theory. This allows stakeholders to understand how each input feature influences a prediction, directly supporting transparency without requiring access to the model's internal structure.

Answer

Combine multiple models to obscure decision logic

Answer

Remove all features except the most predictive ones

Answer

Use black-box models to protect proprietary algorithms

Question 9

A company is building a generative AI application for code generation. They want to minimize costs while maintaining acceptable performance for their workload, which has periodic spikes in demand. Which approach would be MOST cost-effective?

Accepted Answer

Right-size model selection: use a smaller model for simple tasks and a larger model only when needed. Using a smaller, faster model for most requests and a larger model only for complex tasks balances cost and performance, especially with demand spikes.

Answer

Always use the largest available foundation model for all requests

Answer

Use model caching to store responses for repeated prompts

Answer

Use batch inference for all requests

Question 10

A company needs to select a vector store for their Amazon Bedrock Knowledge Base. Which TWO options are supported as vector stores? (Choose TWO.)

Accepted Answer

Amazon Aurora pgvector. Amazon Bedrock Knowledge Bases support Amazon OpenSearch Serverless, Aurora pgvector, Pinecone, and MongoDB Atlas. DynamoDB and Redshift are not vector stores.

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Answer

Amazon DynamoDB