AIF-C01 Practice Test 29 — 15 Questions

Question 1

A company is developing an AI recruitment tool that screens candidates. They want to minimize bias and ensure compliance with emerging regulations like the EU AI Act. Which THREE measures should they implement? (Select THREE.)

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

Accepted Answer

Disclose to candidates that AI is used in the screening process. Conducting bias audits, documenting model cards, and ensuring transparency when AI is used are key practices for fairness, governance, and compliance with regulations like the EU AI Act which classifies recruitment as high-risk.

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Remove all human involvement from the decision process

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Use a single demographic group for training data

Question 2

A startup is building a recommendation engine for their e-commerce platform. They need a fully managed service that can generate personalized product recommendations based on user behavior. Which AWS service should they use?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service specifically designed to generate real-time personalized product recommendations by processing user behavior data (e.g., clicks, purchases, views) and item metadata. It uses the same technology that powers Amazon.com's recommendation engine, making it the correct choice for this e-commerce use case.

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Amazon Rekognition

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Amazon Forecast

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Amazon Comprehend

Question 3

Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)

Accepted Answer

AWS Glue. AWS Glue (A) is correct because it is a fully managed, serverless ETL service that can run Apache Spark jobs and Glue Studio/DynamicFrames to clean, transform, join, and normalize raw data into feature-ready datasets for ML training. Amazon SageMaker Data Wrangler (C) is correct because it is purpose-built for ML data preparation, providing a visual interface with 300+ built-in transforms, data quality/insight reports, and one-click export to SageMaker pipelines or feature stores. Amazon Athena (B) is a serverless interactive query service over S3 using SQL, suited for ad hoc analytics rather than building reusable preprocessing pipelines. Amazon Redshift (D) is a data warehouse for analytics and SQL workloads, not a dedicated ML preprocessing service. AWS Lambda (E) is a general-purpose serverless compute service that could run small transformation code but is not a data preprocessing service for ML.

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Amazon Athena

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Amazon Redshift

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AWS Lambda

Question 4

A healthcare startup is deploying an AI system to assist in diagnosing skin conditions from images. They want to follow the NIST AI Risk Management Framework. Which THREE practices should they implement?

Accepted Answer

Use Amazon SageMaker to continuously monitor model performance and retrain as needed. Option A is correct because the NIST AI RMF's MEASURE and MANAGE functions require ongoing monitoring of model performance in production and retraining when drift or degradation occurs, and Amazon SageMaker Model Monitor plus retraining pipelines directly support this continuous risk-management lifecycle. Option B is correct because the MAP function calls for documenting intended purpose, performance characteristics, and known limitations; a model card is the standard artifact for this transparency and is especially critical in a clinical context. Option C is correct because the MANAGE function emphasizes human oversight of AI decisions, and a human-in-the-loop process for uncertain diagnoses ensures a qualified clinician reviews low-confidence outputs, reducing patient-safety risk. Option D is not required by the NIST AI RMF; long-term archival in S3 Glacier is a data-retention choice that does not by itself address AI risk management, and the framework does not mandate any specific storage class. Option E is also not a framework requirement; AWS Lambda serverless inference is an architectural deployment decision, and the NIST AI RMF is technology-agnostic, so it neither prescribes nor favors Lambda over other inference options.

Answer

Archive all training data in Amazon S3 Glacier for long-term retention

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Deploy the model on AWS Lambda for serverless inference

Question 5

Which of the following is a primary benefit of using Bedrock Agents for building generative AI applications?

Accepted Answer

Orchestrate multi-step tasks and call external APIs via action groups. The primary benefit of using Bedrock Agents is their ability to orchestrate multi-step tasks and call external APIs through action groups. This allows the agent to break down complex user requests into a sequence of actions, invoke APIs, and return results, enabling sophisticated automation.

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Automatically fine-tune the underlying model on new data

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Optimize prompts automatically without any manual tuning

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Guarantee that the model's responses are factually correct

Question 6

A developer is building an agent using Amazon Bedrock Agents to handle customer support inquiries. The agent needs to look up order status from a database and escalate complex issues to a human. Which THREE components are essential for this agent?

Accepted Answer

A Lambda function that connects to the database and returns results. Option A is correct because Amazon Bedrock Agents invoke AWS Lambda functions as action executors to run custom business logic, such as querying an order-status database and returning structured results to the agent. Option E is correct because an action group defines the agent's callable APIs, and its OpenAPI schema tells the agent the available operations, parameters, and request/response format needed to query the database. Option D is correct because a knowledge base (backed by Amazon Bedrock Knowledge Bases and typically Amazon OpenSearch Serverless or another vector store) lets the agent retrieve troubleshooting documentation via RAG so it can answer support questions and decide when to escalate. Option B is not needed because Bedrock Agents work with existing foundation models and do not require a domain-specific fine-tuned model. Option C is not needed because training a custom model on historical tickets is not a required component; the agent uses foundation models plus action groups and knowledge bases.

Answer

A fine-tuned foundation model specific to the support domain

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A custom model trained on historical support tickets

Question 7

A data scientist is fine-tuning a large language model on Amazon SageMaker for a text summarization task. The training loss decreases steadily but the validation loss starts increasing after a few epochs. What should the scientist do to address this issue?

Accepted Answer

Use early stopping based on validation loss. The validation loss increasing while training loss decreases is a classic sign of overfitting. Early stopping based on validation loss halts training when the validation loss stops improving, preventing overfitting and saving computational resources. This is a standard technique in SageMaker's built-in training algorithms and custom training scripts.

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Reduce the batch size

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Increase the learning rate

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Increase the number of training epochs

Question 8

A company wants to log all model invocation requests in Amazon Bedrock for audit and troubleshooting. Which TWO destinations can they configure for invocation logging? (Choose 2)

Accepted Answer

Amazon CloudWatch Logs. Amazon Bedrock invocation logging supports two destination types: Amazon CloudWatch Logs (option A) and Amazon S3 (option B). Option A is correct because Bedrock can deliver text and image invocation logs to a CloudWatch Logs log group, enabling real-time monitoring and troubleshooting via CloudWatch. Option B is correct because Bedrock can also deliver invocation logs to an S3 bucket for durable, long-term audit storage and later analysis. Option C is incorrect because DynamoDB is not a supported invocation logging destination for Bedrock. Option D is incorrect because CloudTrail records API activity/management events, not the model invocation request/response payloads that invocation logging captures. Option E is incorrect because Kinesis Data Firehose is not a configurable destination for Bedrock invocation logging.

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Amazon DynamoDB

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AWS CloudTrail

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Amazon Kinesis Data Firehose

Question 9

A company is deploying an AI-powered document summarization system using Amazon Bedrock. They must ensure that the model only uses information from provided source documents and does not generate unsupported claims. Which Bedrock Guardrails feature should they enable?

Accepted Answer

Contextual grounding check. Contextual grounding checks ensure that model responses are factually supported by the reference sources, reducing hallucinations.

Answer

Content filtering with category-based harmful content filters

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Word filters

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Topic restrictions

Question 10

A company is developing a generative AI application for content creation. They want to ensure transparency as per responsible AI guidelines. Which THREE practices should they implement? (Choose three.)

Accepted Answer

Label AI-generated content with a clear disclosure. Option B is correct because labeling AI-generated content with a clear disclosure directly supports transparency, allowing users to know when content was produced by a generative AI system rather than a human. Option C is correct because providing a disclaimer about the model's capabilities and limitations helps users understand what the system can and cannot reliably do, which is a core responsible AI transparency practice. Option E is correct because documenting the training data sources and potential biases makes the model's provenance and known risks visible to stakeholders, supporting accountability and informed use. Option A is not appropriate because encouraging unquestioning trust undermines transparency and responsible AI principles. Option D, while a valid responsible AI practice for fairness, focuses on ongoing bias monitoring rather than the transparency-specific disclosure and documentation requirements described in the scenario.

Answer

Encourage users to trust the AI outputs without question

Answer

Monitor the model for bias in production

Question 11

A media company is using Amazon Bedrock to generate captions for images. They have a batch processing pipeline that sends thousands of images daily to the Bedrock API using the Titan Image Generator G1 model. Recently, they started receiving ThrottlingException errors during peak hours. The team needs to process all images within 24 hours without changing the model or the application code. The current account has a default quota of 10 requests per second (RPS) for the Titan model in us-east-1. The team estimates they need 50 RPS during peak hours. They have already implemented exponential backoff in the client, but the errors persist. What is the MOST effective solution to resolve the throttling issue?

Accepted Answer

Request a service quota increase for the InvokeModel API for the Titan model in us-east-1. The team has already implemented exponential backoff, but the errors persist because their current quota of 10 RPS is insufficient for the required 50 RPS. Requesting a service quota increase for the InvokeModel API for the Titan Image Generator G1 model in us-east-1 directly addresses the root cause by raising the throughput limit, allowing the existing application code and model to handle the peak load without any architectural changes.

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Use Amazon SageMaker batch transform to process images offline

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Distribute the requests across multiple AWS Regions

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Switch to a different foundation model that has a higher default quota

Question 12

A social media company deploys a content moderation model. They want to minimize the risk of over-censoring legitimate posts (false positives) while still catching harmful content. Which metric should they prioritize?

Accepted Answer

Precision. Precision measures the proportion of positive identifications that are actually correct. High precision means fewer false positives, which aligns with the goal of not over-censoring legitimate posts.

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F1 score

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Recall

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Accuracy

Question 13

Which AWS service can convert a text document from one language to another?

Accepted Answer

Amazon Translate. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and customizable language translation. It uses deep learning models to translate text documents from a source language to a target language, making option D the correct choice for this task.

Answer

Amazon Polly

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Amazon Textract

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Amazon Comprehend

Question 14

A company uses Amazon Bedrock to build a conversational AI. They want to enforce role-based access to the model. Which AWS service should they use?

Accepted Answer

AWS Identity and Access Management (IAM). AWS Identity and Access Management (IAM) is the correct service because it enables fine-grained, role-based access control (RBAC) to Amazon Bedrock models. You can define IAM policies that specify which principals (users, groups, or roles) are allowed to invoke specific foundation models, ensuring that only authorized roles can interact with the conversational AI.

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AWS Config

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AWS CloudTrail

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AWS Organizations

Question 15

A company uses Amazon Bedrock with Anthropic Claude to generate customer-facing content. They must ensure the model does not produce harmful or biased outputs. Which THREE approaches should they implement? (Select THREE.)

Accepted Answer

Use few-shot prompting with examples of safe and appropriate responses. Option A is correct because few-shot prompting supplies the model with concrete exemplars of safe, appropriate responses, which steers Claude toward the desired output style and reduces the likelihood of harmful or biased generations. Option C is correct because Amazon Bedrock Guardrails provides configurable content filters (for hate, violence, sexual, insults, misconduct, and prompt-attack categories) plus denied topics and word filters that actively block or mask unsafe model outputs at inference time. Option E is correct because system prompts establish persistent instructions and safety guidelines that condition every response, giving the model explicit behavioral constraints for customer-facing content. Option B is incorrect because removing content moderation increases the risk of harmful outputs rather than mitigating it. Option D is incorrect because temperature 0.0 only makes sampling more deterministic; it does not prevent harmful or biased content from being generated.

Answer

Remove all content moderation to avoid interfering with model creativity

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Set temperature to 0.0 to eliminate all variability