AIF-C01 Practice Test 1 — 30 Questions

Question 1

A team is deploying a generative AI model for medical report generation. They must ensure patient data privacy and comply with HIPAA. Which AWS service feature is essential for de-identifying protected health information (PHI) before sending data to a foundation model?

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

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is the correct service because it is specifically designed to extract and de-identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, and medical record numbers, and then redact or replace them before the data is sent to a foundation model, ensuring HIPAA compliance.

Answer

AWS CloudHSM

Answer

Amazon Macie

Answer

AWS Key Management Service (AWS KMS)

Question 2

A developer is using the Amazon Bedrock Converse API to build a multi-turn conversational AI. They need to send a user message along with system instructions and previous conversation history. How should they structure the API request to include both system prompt and message history?

Accepted Answer

Include system prompt in the 'system' parameter and user/assistant messages in the 'messages' array. The Amazon Bedrock Converse API separates system prompts from the conversation history. System prompts are passed in the dedicated 'system' parameter, while user and assistant messages are placed in the 'messages' array. This design allows the model to distinguish persistent instructions from the ongoing dialogue, ensuring system-level guidance is not treated as part of the conversation context.

Answer

Include system prompt as a user message in the 'messages' array

Answer

Include system prompt as an assistant message in the 'messages' array

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Include system prompt in the 'inferenceConfig' parameter

Question 3

A company uses Amazon Bedrock to run a question-answering system over a large internal knowledge base. They currently use a RAG approach with Titan Embeddings to index documents and a separate LLM for generation. The team notices that the retrieval often returns irrelevant chunks, causing the LLM to produce incorrect answers. Which action would MOST directly improve retrieval relevance?

Accepted Answer

Switch from Titan Embeddings to a more powerful embedding model like Cohere Embed on Bedrock. The core issue is that the retrieval step is returning irrelevant chunks, which directly undermines the RAG pipeline. Switching from Titan Embeddings to a more powerful embedding model like Cohere Embed on Bedrock improves the semantic quality of the vector representations, leading to more relevant document retrieval. This directly addresses the root cause of poor retrieval relevance, whereas other options focus on generation or chunking parameters that do not fix the embedding quality.

Answer

Add a system prompt instructing the LLM to only answer when confident

Answer

Reduce the chunk size of documents to 256 tokens

Answer

Increase the temperature of the LLM to 0.7

Question 4

A financial services company is deploying a generative AI chatbot to assist customers with account inquiries. The company wants to ensure the chatbot does not generate biased or harmful responses. Which combination of AWS services and practices should the company implement to monitor and mitigate these risks?

Accepted Answer

Use Amazon SageMaker Clarify to detect bias in model outputs and implement a human-in-the-loop workflow with Amazon A2I to review flagged responses.. Amazon SageMaker Clarify is specifically designed to detect bias in machine learning models and their outputs, while Amazon Augmented AI (A2I) enables a human-in-the-loop workflow to review flagged responses. This combination directly addresses the requirement to monitor and mitigate biased or harmful responses from a generative AI chatbot, ensuring responsible AI practices.

Answer

Configure the chatbot to use a pre-trained model from SageMaker JumpStart and disable logging to avoid storing sensitive customer data.

Answer

Use Amazon Rekognition to analyze chat logs for biased language and automatically block responses with a confidence score above 90%.

Answer

Deploy Amazon Lex with built-in sentiment analysis to detect negative customer emotions and automatically escalate to a human agent.

Question 5

A data science team wants to document and share their model's intended use, performance, and limitations with stakeholders. They also need to track the model's version and deployment history. Which TWO AWS services or features should they use?

Accepted Answer

Amazon SageMaker Model Cards. SageMaker Model Cards provide standardized documentation for transparency. SageMaker Model Registry tracks model versions, deployment stages, and metadata. SageMaker Pipelines is for ML workflows, not documentation or version tracking. SageMaker Studio is an IDE. SageMaker Clarify is for bias and explainability.

Answer

Amazon SageMaker Pipelines

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Studio

Question 6

A company uses Amazon Bedrock Agents to automate order processing. The agent needs to call an internal database to check inventory. Which component should be used to integrate the database query?

Accepted Answer

Action group. Bedrock Agents use action groups to define external tools or APIs. Each action group has an associated AWS Lambda function that performs the actual work (e.g., querying a database). The agent can call the action group via natural language instructions.

Answer

Knowledge base

Answer

Guardrail

Answer

Prompt template

Question 7

A developer wants to create an agent using Amazon Bedrock Agents that can call an external API to check inventory levels. What must be defined in the agent configuration to enable this API call?

Accepted Answer

An action group. Action groups define the set of actions (API calls) that an agent can perform. Each action group specifies the API schema and optionally a Lambda function for execution.

Answer

A knowledge base

Answer

A guardrail

Answer

A prompt template

Question 8

A company wants to use AWS services to process natural language text. Which TWO AWS services provide natural language processing (NLP) capabilities? (Select TWO.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend (C) is correct because it is AWS's fully managed NLP service that uses machine learning to extract insights from text, including sentiment analysis, entity recognition, key phrase extraction, language detection, and topic modeling. Amazon Lex (E) is correct because it provides NLP capabilities for building conversational interfaces (chatbots and voice bots) using automatic speech recognition and natural language understanding to interpret user intent and slot values. Amazon Translate (A) is not marked correct because it performs language translation rather than general NLP analysis, even though it is a text-processing service. Amazon Rekognition (B) is not marked correct because it is a computer vision service for image and video analysis, not natural language text. Amazon Polly (D) is not marked correct because it is a text-to-speech service that converts written text into lifelike speech, which is speech synthesis rather than NLP understanding.

Answer

Amazon Translate

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Amazon Rekognition

Answer

Amazon Polly

Question 9

A developer is building a RAG application on Amazon Bedrock. They notice that the model sometimes generates answers that are not supported by the retrieved documents. To reduce this, they want to enforce that the model only uses the provided context. Which Bedrock feature should they use?

Accepted Answer

Enable 'grounding' in Bedrock Guardrails. Amazon Bedrock Guardrails includes a contextual grounding check that evaluates whether a model's response is factually supported by the source context (the retrieved documents) and whether it is relevant to the user's query. By enabling grounding in Guardrails, the developer can configure thresholds that filter or flag responses with low grounding scores, directly reducing hallucinations. This is the purpose-built Bedrock feature for enforcing that answers stay within the provided context.

Answer

Increase the number of retrieved documents

Answer

Use a larger context window model

Answer

Adjust the temperature parameter to 0

Question 10

A data scientist needs to train a model in Amazon SageMaker using a dataset that contains personally identifiable information (PII). The company policy requires all data at rest to be encrypted with a customer-managed key. Which configuration meets this requirement?

Accepted Answer

Specify a KMS customer-managed key when creating the SageMaker training job and enable data encryption for the S3 bucket with the same key. Using a KMS customer-managed key (CMK) for SageMaker's EBS volumes and S3 buckets ensures encryption at rest with a key the customer controls. The other options either use AWS-managed keys, skip encryption, or are not applicable to SageMaker training jobs.

Answer

Enable default encryption for the S3 bucket using AWS-managed S3 keys (SSE-S3)

Answer

Encrypt the data before uploading to S3 using a client-side library

Answer

Use SageMaker's local mode and store data on the instance's ephemeral storage

Question 11

A company is using Amazon Bedrock to generate content for a marketing application. The company wants to ensure that the model does not generate content that violates the company's brand guidelines, which prohibit certain keywords and tones. Which TWO features should the company use to enforce these guidelines? (Choose two.)

Accepted Answer

Create a prompt template that instructs the model to adhere to brand guidelines and avoid prohibited keywords.. Prompt engineering allows the company to embed brand guidelines directly into the instruction given to the model, effectively steering the output away from prohibited keywords and tones. Option C is correct because Amazon Bedrock Guardrails provides a managed, policy-based mechanism to define custom deny topics and content filters that can block or mask unwanted content at inference time, enforcing brand guidelines without manual intervention.

Answer

Enable Amazon CloudWatch Logs to capture model output and manually review.

Answer

Use AWS IAM policies to restrict the model's output to only approved words.

Answer

Encrypt the model responses using AWS KMS to prevent unauthorized viewing.

Question 12

Which AWS service provides access to a wide variety of foundation models from different providers through a single API, without managing underlying infrastructure?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to a wide variety of foundation models (FMs) from leading AI providers such as Anthropic, Meta, Stability AI, and Amazon itself through a single API. It eliminates the need to manage underlying infrastructure, such as GPU clusters or model hosting, allowing developers to integrate generative AI capabilities directly into applications.

Answer

Amazon SageMaker

Answer

AWS Lambda

Answer

Amazon Rekognition

Question 13

A healthcare organization uses an ML model to predict patient readmission risk. The model performs well overall but has significantly higher false negative rates for elderly patients. The team needs to mitigate this bias. Which step should they take FIRST?

Accepted Answer

Use SageMaker Clarify to compute bias metrics and identify the extent of the disparity. The first step is to identify and understand the bias through measurement. SageMaker Clarify can compute fairness metrics like false negative rate difference to quantify the disparity. Retraining with fairness constraints or reweighting are mitigation steps that come after measurement.

Answer

Retrain the model with fairness constraints using SageMaker Clarify

Answer

Remove the age feature from the model

Answer

Collect more data for elderly patients to reduce representation bias

Question 14

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

Accepted Answer

Enable PII redaction in the Bedrock guardrails.. Amazon Bedrock Guardrails provide a configurable content filtering and PII redaction feature that can automatically detect and mask personally identifiable information (PII) in model inputs and outputs. By enabling PII redaction within guardrails, the startup can ensure that sensitive data like names, addresses, or credit card numbers are removed or obfuscated before the virtual assistant's responses reach the user. This is the direct and intended mechanism for preventing PII leakage in model responses.

Answer

Enable model invocation logging.

Answer

Configure a VPC endpoint.

Answer

Enable data encryption at rest.

Question 15

A company needs to audit all API calls made to Amazon Bedrock, including model invocations and guardrail evaluations. Which AWS service should they enable to capture these API calls for compliance?

Accepted Answer

AWS CloudTrail. AWS CloudTrail records API calls for all AWS services, including Bedrock. It captures the caller identity, API, parameters, and response elements, which can be used for auditing and compliance.

Answer

Amazon Macie

Answer

AWS Config

Answer

Amazon GuardDuty

Question 16

A team is fine-tuning a foundation model using SageMaker. They want to minimize training time while keeping the model's original knowledge. Which technique is BEST suited?

Accepted Answer

Use Parameter Efficient Fine-Tuning (PEFT) such as LoRA. Parameter Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are best suited because they freeze the pre-trained model weights and inject trainable low-rank matrices into specific layers, drastically reducing the number of trainable parameters. This minimizes training time and computational cost while preserving the model's original knowledge, as only a small fraction of parameters are updated during fine-tuning.

Answer

Use distributed training across multiple GPUs

Answer

Use prompt engineering instead of fine-tuning

Answer

Full fine-tuning on the new dataset

Question 17

Which of the following is an example of reinforcement learning?

Accepted Answer

A robot learning to navigate a maze by receiving rewards for reaching the goal. Reinforcement learning involves an agent learning to make decisions by interacting with an environment and receiving rewards or penalties for its actions. Option D describes a robot learning to navigate a maze by receiving rewards for reaching the goal, which is a classic example of an agent optimizing its policy through trial and error to maximize cumulative reward.

Answer

Predicting house prices based on historical data

Answer

Grouping customers into segments based on purchasing behavior

Answer

Detecting spam emails using labeled examples

Question 18

A company runs a chatbot using a large language model on Amazon Bedrock. They notice high latency during peak hours. Which action would be MOST effective to reduce latency without degrading response quality?

Accepted Answer

Use Provisioned Throughput for model inference. Provisioned Throughput on Amazon Bedrock reserves dedicated capacity for model inference, ensuring consistent low latency even during peak hours. This eliminates the variability caused by resource contention in the on-demand tier, directly addressing high latency without altering model size or output quality.

Answer

Increase the number of concurrent invocations

Answer

Switch to a smaller model

Answer

Decrease the maxTokens parameter

Question 19

A company is building a resume screening model and discovers that the training data contains only resumes from one gender, leading to biased predictions. Which type of bias does this represent, and what is the most effective mitigation strategy?

Accepted Answer

Representation bias; mitigate by collecting more diverse training data. Representation bias occurs when certain groups are underrepresented in the training data. Mitigation includes collecting more diverse data or using techniques like re-weighting or synthetic data generation.

Answer

Aggregation bias; mitigate by using a single model for all groups

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Measurement bias; mitigate by using more precise measurement tools

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Historical bias; mitigate by removing sensitive attributes from the model

Question 20

A company needs to summarize thousands of customer reviews daily using a foundation model. The solution must minimize latency and cost while handling variable traffic. Which AWS service should they use?

Accepted Answer

Amazon Bedrock with on-demand mode. Amazon Bedrock with on-demand mode is correct because it provides serverless access to foundation models (FMs) with pay-per-use pricing, which minimizes cost for variable traffic and eliminates the need to provision infrastructure. The on-demand mode handles thousands of daily summarization requests with low latency by leveraging AWS's scalable inference infrastructure, making it ideal for variable workloads without upfront commitments.

Answer

Amazon SageMaker real-time endpoint

Answer

Amazon Comprehend

Answer

Amazon Lex