AIF-C01 Practice Test 31 — 15 Questions

Question 1

A company is using an LLM to generate customer support responses. They want to reduce hallucinations and improve the accuracy of the responses. Which TWO approaches are most effective? (Select TWO.)

Accepted Answer

Apply Bedrock Guardrails with contextual grounding check. RAG grounds the model in retrieved facts, while Bedrock Guardrails with contextual grounding check validates responses against sources. Both are proven techniques to reduce hallucinations.

Answer

Increase the temperature parameter to 1.0

Answer

Use a smaller model to reduce complexity

Answer

Remove all system prompts

Question 2

A financial services company uses a machine learning model to approve loan applications. The model is a gradient boosting classifier trained on historical loan data. Recently, the company noticed that the model's approval rate for applicants from a certain demographic group is significantly lower than for other groups, even though the model's overall accuracy remains high. The data science team has been asked to address this potential bias while minimizing the impact on overall model performance. The team has access to the training data and the trained model. They have limited time and budget. Which course of action should the team take first?

Accepted Answer

Analyze the training data for bias and retrain the model using bias mitigation techniques such as reweighting.. The first step should be to analyze the training data for bias and then retrain the model using bias mitigation techniques such as reweighting, because this addresses the root cause of the disparity while aiming to preserve overall performance. Reweighting adjusts sample weights to reduce the influence of biased historical patterns, and it can be applied with limited time and budget since it works with existing data and model. This is a targeted, cost-effective first action before considering more expensive data collection or post-deployment adjustments.

Answer

Remove the sensitive attribute from the training data and retrain the model.

Answer

Collect more data from the under-represented demographic group and retrain the model.

Answer

Adjust the model's decision threshold for the affected group after deployment.

Question 3

A startup uses Amazon Bedrock with a provisioned throughput to generate product images. They now have unpredictable traffic and want to reduce costs. What should they do?

Accepted Answer

Switch to on-demand mode in Amazon Bedrock.. On-demand mode in Amazon Bedrock allows you to pay per inference request without committing to a provisioned throughput, making it ideal for unpredictable traffic patterns. This eliminates the cost of idle capacity while still providing access to the same foundation models. Option D directly addresses the need to reduce costs when traffic is variable.

Answer

Switch to batch inference using Amazon Bedrock.

Answer

Keep the provisioned throughput but reduce the number of units.

Answer

Use a different model or service like Amazon SageMaker with spot instances.

Question 4

A developer is using Bedrock Studio to prototype a summarization application. They want to quickly test different foundation models and prompts without writing code. What should they use?

Accepted Answer

Bedrock Playground. Amazon Bedrock Playground provides an interactive, no-code environment where developers can experiment with different foundation models and prompts. It allows quick testing and comparison of model outputs without writing any code, making it ideal for prototyping a summarization application.

Answer

Bedrock Knowledge Bases

Answer

Bedrock Agents

Answer

Bedrock Guardrails

Question 5

Which vector store is a fully managed AWS service that can be used with Amazon Bedrock Knowledge Bases for semantic search?

Accepted Answer

Amazon OpenSearch Serverless. Amazon OpenSearch Serverless is a fully managed AWS service that provides a vector store capability, which is required for semantic search in Amazon Bedrock Knowledge Bases. It supports vector indexing and similarity search, enabling efficient retrieval of relevant documents based on embedding vectors. Other options like DynamoDB, RDS for MySQL, and S3 are not purpose-built vector stores and lack the native vector search functionality needed for this use case.

Answer

Amazon DynamoDB

Answer

Amazon RDS for MySQL

Answer

Amazon S3

Question 6

An enterprise wants to ensure that generative AI applications built on AWS comply with data privacy regulations. They need to prevent the model from using customer data in future training. Which feature of Amazon Bedrock should they enable?

Accepted Answer

Opt-out of model improvement. Amazon Bedrock's opt-out of model improvement feature allows customers to prevent AWS from using their data (including prompts, completions, and associated metadata) for model training or service improvement. This is essential for compliance with data privacy regulations like GDPR or CCPA, as it ensures customer data is not retained or used beyond the immediate inference request.

Answer

Policy-based data governance

Answer

Data encryption at rest

Answer

Model customization with customer data

Question 7

A company uses Amazon SageMaker to build a binary classification model for loan approvals. After training, the data science team wants to evaluate the model for potential bias against a protected group. Which AWS service should they use to compute bias metrics?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify is the correct service because it is specifically designed to detect bias in machine learning models and datasets. It provides built-in bias metrics (e.g., difference in positive proportion, disparate impact) for both pre-training and post-training evaluation, making it the appropriate tool for assessing potential bias against a protected group in a binary classification model.

Answer

Amazon SageMaker Model Monitor

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Experiments

Question 8

Which TWO of the following are key advantages of using Amazon Bedrock for building generative AI applications?

Accepted Answer

Ability to fine-tune models using your own data without managing underlying infrastructure.. Option B is correct because Amazon Bedrock is a fully managed service that lets you customize (fine-tune) foundation models with your own data while AWS handles the underlying infrastructure, so you never provision or manage servers or clusters. Option E is correct because Bedrock provides a single, unified API to access multiple foundation models from different providers such as Anthropic, AI21 Labs, Cohere, Meta, Stability AI, and Amazon, which simplifies multi-model development. Option A is wrong because Bedrock does not automatically optimize prompts for all models without user intervention; prompt engineering remains the developer's responsibility. Option C is wrong because data preprocessing is still required for many use cases, especially when preparing fine-tuning datasets or cleaning inputs. Option D is wrong because generative models are probabilistic and do not guarantee identical outputs for the same prompt, even with low temperature settings.

Answer

Automatic optimization of prompts for all models without user intervention.

Answer

Eliminates the need for any data preprocessing before model invocation.

Answer

Guaranteed identical outputs from all models for the same prompt.

Question 9

Which of the following is NOT one of the core principles of responsible AI as defined by AWS?

Accepted Answer

Profitability. Profitability is not one of the core principles of responsible AI as defined by AWS. AWS defines six core principles: Fairness, Transparency, Robustness, Privacy, Security, and Explainability. Profitability is a business objective, not a principle for ensuring ethical and trustworthy AI systems.

Answer

Transparency

Answer

Fairness

Answer

Robustness

Question 10

A data scientist is using Amazon SageMaker to train a model and wants to understand the contribution of each feature to individual predictions. Which technique should they use to generate local explanations?

Accepted Answer

SHAP values. SHAP (SHapley Additive exPlanations) values are the correct choice because they provide local explanations by decomposing a prediction into the additive contribution of each feature, based on cooperative game theory. This allows the data scientist to understand exactly how each feature influenced a specific individual prediction, unlike global methods that summarize behavior across the entire dataset.

Answer

Permutation feature importance

Answer

Global feature importance

Answer

Partial dependence plots

Question 11

A company is deploying an AI-based diagnostic system in healthcare. Which THREE practices align with AWS responsible AI guidelines? (Choose THREE.)

Accepted Answer

Continuously monitor model performance for drift using SageMaker Model Monitor.. AWS Responsible AI guidelines emphasize several practices for high-risk systems like healthcare diagnostics. Continuous monitoring with SageMaker Model Monitor helps detect data quality, bias, and feature attribution drift, supporting reliability and safety (B). Documenting the model's intended use, limitations, and performance with model cards promotes transparency and accountability (D). Implementing a human-in-the-loop review process using Amazon Augmented AI (A2I) ensures meaningful human oversight for high-risk predictions (E). In contrast, deploying without manual review (A) and relying solely on automated decisions (C) violate responsible AI principles requiring human oversight and validation.

Answer

Deploy the model in production immediately after training without manual review.

Answer

Use only automated decision-making without any human oversight.

Question 12

Refer to the exhibit. A data scientist runs SageMaker Clarify on a training dataset and receives the above JSON output. Which bias metric exceeds its threshold?

Accepted Answer

DPL. The JSON output shows DPL (Demographic Parity Difference) = 0.15, which exceeds the commonly used threshold of 0.10. DPL measures the difference in the probability of a favorable outcome between advantaged and disadvantaged groups; a value above 0.10 indicates significant bias. The other metrics (Label Imbalance = 0.05, Class Imbalance = 0.02) are below their respective thresholds, so only DPL triggers the violation.

Answer

Label Imbalance

Answer

Class Imbalance

Answer

All metrics exceed thresholds

Question 13

A healthcare company is using Amazon SageMaker to deploy a model that makes predictions on patient data. They need to ensure that the model's predictions are explainable to comply with regulations. Which approach should they take?

Accepted Answer

Use SageMaker Clarify to generate feature importance and explanations. SageMaker Clarify is specifically designed to provide model explainability, including feature importance and SHAP-based explanations, which are essential for regulatory compliance in healthcare. It helps stakeholders understand why a model made a particular prediction, addressing transparency requirements.

Answer

Use SageMaker Model Monitor to track predictions

Answer

Use SageMaker Experiments to log model parameters

Answer

Use SageMaker Debugger to analyze training gradients

Question 14

Which THREE practices are recommended for promoting robustness and security in AI systems?

Accepted Answer

Implement strong access controls and encryption for model artifacts. Robustness and security in AI systems require multiple complementary practices. (B) Implementing strong access controls (e.g., IAM policies, role-based access control) and encryption (e.g., AES-256 for data at rest, TLS 1.2+ for data in transit) protects model artifacts from unauthorized access, tampering, and exfiltration, ensuring confidentiality and integrity throughout the lifecycle. (C) Regularly testing the model against adversarial examples surfaces vulnerabilities to evasion, poisoning, and prompt-injection style attacks, allowing defenses to be hardened before real-world exploitation. (D) Monitoring model performance for data drift and concept drift detects when the model's inputs or the underlying relationships change, so degradation, unexpected behavior, or emerging security-relevant anomalies can be caught and remediated early. Together these practices cover artifact protection, adversarial resilience, and ongoing operational vigilance.

Answer

Deploy the model immediately after training without validation

Answer

Remove logging and monitoring to improve performance

Question 15

A retail company is deploying a chatbot to handle customer inquiries. During testing, they notice the chatbot occasionally uses offensive language when responding to certain user inputs. Which responsible AI principle is being violated?

Accepted Answer

Fairness. The chatbot's use of offensive language in responses indicates a bias in the model's training data or behavior, which violates the Fairness principle. Fairness in responsible AI requires that systems do not discriminate against or harm individuals or groups based on protected characteristics, and generating offensive outputs directly undermines this. This is distinct from privacy (data protection), transparency (explainability), or accountability (responsibility for outcomes).

Answer

Privacy

Answer

Transparency

Answer

Accountability