AIF-C01 Practice Test 42 — 15 Questions

Question 1

A company uses Amazon Bedrock Agents to automate a multi-step data processing workflow. The agent needs to call an external API to enrich customer records. How should the developer expose this API to the agent?

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

Accepted Answer

By defining an action group with the API specification and a Lambda function. Action groups define the tools an agent can invoke. Each action group contains Lambda functions or API schemas that the agent calls during execution.

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By embedding the API directly in the agent's prompt instructions

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By creating a custom model that has been fine-tuned to call the API

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By configuring a Bedrock Knowledge Base with API documentation

Question 2

A company is using Bedrock Agents to automate multi-step workflows that interact with external APIs and databases. They need to ensure the agent can perform actions like querying a database and calling an API. Which TWO components must be defined to enable these capabilities? (Choose TWO)

Accepted Answer

Lambda functions that implement the business logic for each action. Action groups define the tools (APIs, databases) the agent can call, and Lambda functions implement the logic. Knowledge Bases are for document retrieval, Guardrails for safety, and prompt flows for prompt management.

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A prompt flow to manage multiple prompts

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Bedrock Guardrails to filter inappropriate actions

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A Bedrock Knowledge Base to store static documents

Question 3

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.

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Amazon DynamoDB

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Amazon RDS for MySQL

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Amazon S3

Question 4

A team is building an AI chatbot that will be used by customers with visual impairments. Which design practice best supports inclusive accessibility for this user group?

Accepted Answer

Ensure the chatbot is fully navigable via keyboard and compatible with screen readers. Ensuring full keyboard navigability and compatibility with screen readers is a fundamental accessibility requirement for users with visual impairments, enabling them to interact without relying on visual cues.

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Add captions to all video content

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Provide a voice-only interface without any text output

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Use high-contrast color schemes and large fonts

Question 5

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 6

A developer needs to ensure that a generative AI application on Amazon Bedrock does not produce harmful or inappropriate content. Which feature should they configure?

Accepted Answer

Guardrails for Amazon Bedrock. Guardrails for Amazon Bedrock is the correct feature because it allows developers to define policies that filter and block harmful or inappropriate content in both user inputs and model outputs. This includes configurable thresholds for topics, content filters (e.g., hate, insults, sexual, violence), and denied topics, ensuring the generative AI application adheres to safety and responsible AI requirements.

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Provisioned Throughput

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Model Invocation Logging

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Knowledge Bases for Amazon Bedrock

Question 7

Which THREE are benefits of using Amazon Bedrock over self-managing foundation models on EC2? (Choose THREE.)

Accepted Answer

Built-in integration with AWS services such as AWS CloudWatch and AWS CloudTrail.. Amazon Bedrock provides built-in integration with AWS services like CloudWatch for monitoring model invocation metrics and CloudTrail for auditing API calls. This eliminates the need to manually set up logging and monitoring infrastructure when self-managing foundation models on EC2, where you would have to configure these integrations yourself.

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Lower data transfer costs between cloud regions.

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Greater control over model fine-tuning and customization.

Question 8

A financial services company uses Bedrock Agents to automate a multi-step loan approval process. The agent needs to call an external credit scoring API and a compliance database, then combine results. The agent currently fails when the API returns a 503 error. How should the practitioner address this?

Accepted Answer

Implement a custom Lambda function for the action group to handle errors and retries. Lambda functions integrated with action groups can implement retry logic, error handling, and custom processing. Granularity tuning or trace truncation does not handle errors. Guardrails are for content filtering, not API error handling.

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Adjust the Bedrock Agent's granularity setting to 'high'

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Reduce the agent's trace truncation limit to shorten the context

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Use Bedrock Guardrails with a topic denial rule for error messages

Question 9

A developer needs to preprocess a dataset consisting of customer reviews for sentiment analysis. Which text preprocessing technique is most likely to improve model accuracy?

Accepted Answer

All of the above. All three listed techniques—stemming, removing stop words, and lowercasing—are standard text preprocessing steps that collectively improve model accuracy for sentiment analysis. Stemming reduces words to root forms to consolidate similar meanings, removing stop words eliminates noise from high-frequency but low-information tokens, and lowercasing normalizes case variations. Together, they reduce the feature space and help the model focus on sentiment-bearing terms, leading to better generalization and accuracy.

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Stemming

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Removing stop words

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Lowercasing

Question 10

Which TWO techniques can reduce the cost of running a fine-tuned foundation model on Amazon SageMaker? (Choose TWO.)

Accepted Answer

Implement structured pruning to remove less important model parameters.. Structured pruning reduces the number of parameters in the model by removing entire neurons, channels, or layers that contribute little to the output. This directly shrinks the model size and computational requirements, leading to lower memory usage and faster inference on SageMaker, which reduces cost.

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Use larger instance types with more GPUs to speed up inference.

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Store the model parameters in FP32 to maintain accuracy during inference.

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Increase the number of training epochs to achieve higher accuracy.

Question 11

A company is using Amazon SageMaker to train machine learning models. The security team wants to ensure that the training data is encrypted at rest and that the SageMaker notebook instances cannot access the internet. Which TWO actions should the company take? (Choose TWO.)

Accepted Answer

Enable S3 server-side encryption with AWS KMS (SSE-KMS) for the training data bucket. Enabling S3 server-side encryption with AWS KMS (SSE-KMS) ensures that the training data stored in the S3 bucket is encrypted at rest. This satisfies the security team's requirement for data encryption at rest, as SSE-KMS provides envelope encryption with a customer-managed or AWS-managed KMS key, giving the company control over the encryption keys and auditability via AWS CloudTrail.

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Create an AWS CloudTrail trail to log all S3 data events

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Enable encryption at rest for the SageMaker endpoint using the AWS Management Console

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Use AWS Security Token Service (STS) to generate temporary credentials for the notebook instance

Question 12

Refer to the exhibit. A team is creating an IAM policy for a SageMaker notebook user. The user needs to access training data in an S3 bucket and create models. Which responsible AI concern is most relevant to this policy?

Accepted Answer

The policy grants overly broad permissions, violating the principle of least privilege.. The IAM policy grants the SageMaker notebook user overly broad permissions (e.g., s3:* or sagemaker:*), which violates the principle of least privilege. This is a core responsible AI concern as it increases the risk of unauthorized access or accidental modification of training data and models. The policy should restrict actions to only those necessary for the user's specific role, such as s3:GetObject for specific buckets and sagemaker:CreateModel with resource constraints.

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The policy does not enforce encryption for the notebook.

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The policy does not restrict which S3 buckets the user can read.

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The policy does not include a condition for model explainability.

Question 13

Which TWO of the following are valid methods to reduce the risk of foundation models generating harmful or biased content?

Accepted Answer

Use a content filter. Content filters act as a safety layer that intercepts and blocks harmful or biased outputs before they reach the user. These filters can be rule-based or use a separate classifier model trained to detect toxic, hateful, or biased language, reducing the risk of harmful content generation without altering the underlying model.

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Use a smaller model

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Fine-tune the model on a biased dataset

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Disable all logging

Question 14

A company operates in a region where Amazon Bedrock is not available. They want to use generative AI but must keep data within the country. Which solution should they consider?

Accepted Answer

Use Amazon SageMaker to host an open-source model in the local region.. Amazon SageMaker allows you to host open-source models (e.g., Llama 2, Falcon) in any AWS region, including those where Bedrock is unavailable. This satisfies the data residency requirement because the model and data never leave the local region. SageMaker provides full control over the infrastructure, enabling compliance with local data sovereignty laws.

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Wait for Bedrock to become available in their region; there is no alternative.

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Use Amazon Bedrock in the nearest available region with cross-region inference.

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Use an API from a third-party generative AI provider with AWS PrivateLink.

Question 15

A financial services company uses Amazon Rekognition to verify customer identities. To ensure responsible AI practices, which measure should the company prioritize?

Accepted Answer

Regularly audit the model for demographic bias. Regularly auditing the model for demographic bias is a core responsible AI practice, especially for identity verification systems where biased outcomes could lead to unfair treatment of certain customer groups. Amazon Rekognition's facial analysis and comparison features must be tested across diverse demographics to ensure equitable performance, as bias can arise from imbalanced training data or algorithmic artifacts.

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Use only black-box models to protect intellectual property

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Increase model complexity to improve accuracy

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Minimize the amount of training data collected