AIF-C01 Practice Test 35 — 15 Questions

Question 1

A financial services company is subject to strict regulatory requirements. They plan to use generative AI to summarize customer interaction logs. Which combination of AWS services and configurations best ensures compliance while maintaining accuracy?

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

Accepted Answer

Use Amazon Bedrock with a private VPC endpoint, AWS KMS encryption, and content filtering.. It combines a private VPC endpoint to keep all traffic within the AWS network (avoiding public internet exposure), AWS KMS encryption for data at rest and in transit, and content filtering to block sensitive or non-compliant outputs. This architecture meets strict regulatory requirements for data privacy and security while using Amazon Bedrock's managed foundation models for accurate summarization.

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Deploy an open-source model on Amazon Bedrock in a local on-premises server.

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Use Amazon Bedrock with a foundation model and public internet access without encryption.

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Use Amazon SageMaker to host a fine-tuned model with a public API key.

Question 2

A team trains a model using Amazon SageMaker built-in XGBoost. After training, they want to evaluate feature importance. Which SageMaker feature allows them to view this?

Accepted Answer

SageMaker Debugger. SageMaker Debugger provides built-in monitoring and visualization capabilities, including the ability to capture feature importance metrics (e.g., gain, cover, weight) from XGBoost training jobs. It automatically saves these metrics to Amazon S3 and allows you to view them through the SageMaker Studio Debugger dashboard or by querying the saved tensors, enabling direct evaluation of feature importance without additional custom code.

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SageMaker Experiments

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SageMaker Autopilot

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SageMaker Model Monitor

Question 3

A healthcare company needs to use a foundation model for analyzing medical records while complying with HIPAA. They plan to use Amazon Bedrock. What should they do to meet HIPAA requirements?

Accepted Answer

All of the above. HIPAA compliance in Amazon Bedrock requires a combination of controls: using a HIPAA-eligible model in a region where AWS offers a Business Associate Addendum (BAA), enabling access logging for auditability, and encrypting data at rest and in transit. None of the individual options alone satisfy all HIPAA requirements; only the full set of controls ensures compliance.

Answer

Use a model that is HIPAA eligible in a region that supports BAA

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Implement access logging for all API calls

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Encrypt data at rest and in transit

Question 4

A company is evaluating the output quality of a summarisation model using Amazon Bedrock Model Evaluation. They want to use both automated and human evaluation. Which THREE components can they configure as part of a model evaluation job? (Choose THREE.)

Accepted Answer

A human evaluation workforce configuration. In Bedrock Model Evaluation, you can specify the model, evaluation dataset, automated metrics, and human review workforce. Prompt template and inference parameters are part of the configuration.

Answer

An Amazon Bedrock Agent for orchestration

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A vector store index for retrieval

Question 5

A data scientist is using Amazon SageMaker Studio. The company policy requires that all data used in SageMaker Studio notebooks be encrypted at rest and in transit. Which configuration should be enabled to meet this requirement?

Accepted Answer

Enable SageMaker Studio's data encryption using a customer managed key (CMK) and configure the notebook to use HTTPS for all traffic.. It addresses both encryption at rest and in transit. SageMaker Studio uses Amazon EFS for persistent storage, and enabling encryption at rest with a customer managed key (CMK) ensures data on the EFS file system is encrypted. Additionally, configuring the notebook to use HTTPS ensures all traffic between the notebook and other services is encrypted in transit, meeting the company policy.

Answer

Store all data in encrypted DynamoDB tables and query from notebooks.

Answer

Use a VPC with no internet access and enable S3 encryption for all data.

Answer

Enable SageMaker Studio's default encryption using the AWS managed key for EFS.

Question 6

A company uses Amazon SageMaker to build and deploy machine learning models. The security team has implemented a policy that all SageMaker notebook instances must be launched in a VPC and cannot have direct internet access. However, data scientists need to download open-source datasets from the internet for model training. They also need to install Python packages from PyPI. Currently, the SageMaker notebook instances are in a VPC with no internet gateway. The data scientists cannot download any external data or packages. The VPC has a NAT gateway already configured. What should the company do to allow the necessary internet access while maintaining the security requirement?

Accepted Answer

Add a route from the private subnets to the NAT gateway for 0.0.0.0/0.. The VPC already has a NAT gateway configured, but the private subnets lack a route to it for internet-bound traffic. By adding a route from the private subnets to the NAT gateway for destination 0.0.0.0/0, outbound traffic from the SageMaker notebook instances can reach the internet (e.g., to download datasets from open-source sites and install packages from PyPI) while the instances remain in a private subnet without direct internet access, satisfying the security requirement.

Answer

Attach an internet gateway to the VPC and add a route to the internet gateway in the subnet's route table.

Answer

Place the notebook instances in a public subnet and configure security group rules to restrict inbound traffic.

Answer

Create a VPC endpoint for S3 and a VPC endpoint for PyPI, and route traffic through them.

Question 7

A company wants to send personalized product recommendations to customers based on their browsing history and previous purchases. Which AWS service is BEST suited for this?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service specifically designed to build real-time personalized recommendation systems. It uses the same technology as Amazon.com's recommendation engine, processing user-item interaction data (browsing history, purchases) to generate tailored product suggestions. This makes it the ideal choice for the described use case.

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Amazon SageMaker built-in factorization machines

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Amazon Forecast

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Amazon Rekognition

Question 8

A company uses Amazon SageMaker Model Registry to manage model versions. The security team requires that only approved models can be deployed to production. The ML team creates a new model version and registers it in the Model Registry. When trying to deploy the model to a production endpoint, the deployment fails because the model is not approved. The ML team asks the DevOps team to approve the model. However, the DevOps team cannot see the model in the Model Registry. What is the MOST likely reason?

Accepted Answer

The DevOps team does not have the required IAM permissions to view models in the Model Registry.. The most likely reason the DevOps team cannot see the model in the Model Registry is that they lack the required IAM permissions (e.g., `sagemaker:DescribeModelPackage` or `sagemaker:ListModelPackages`) to view models. Without these permissions, the model is invisible to them in the SageMaker console or API, even though it exists. The deployment failure due to approval status is a separate symptom; the core issue here is visibility, not approval workflow.

Answer

The model artifacts are stored in an S3 bucket that uses SSE-KMS, and the DevOps team does not have KMS decrypt permission.

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The model version is in a 'Pending' approval status and needs to be promoted by the ML team first.

Answer

The Model Registry is in a different AWS account, and the DevOps team does not have cross-account access.

Question 9

A financial institution is building a model to approve loan applications. They must comply with the EU AI Act, which classifies credit scoring as a high-risk AI system. Which requirement is the MOST likely to apply under the EU AI Act?

Accepted Answer

The system must be able to be overridden or stopped by a human. High-risk AI systems under the EU AI Act require human oversight to ensure that decisions can be reviewed and overridden. Transparency and bias reporting are also requirements, but human oversight is specifically mandated to allow intervention.

Answer

The model must be trained exclusively on data from within the EU

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The model must achieve a minimum accuracy of 95%

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The system must be explainable using SHAP values

Question 10

A company wants to ensure that only approved machine learning models are deployed to production on Amazon SageMaker. Which combination of services can enforce this governance requirement?

Accepted Answer

AWS Config custom rules and AWS IAM policies. AWS Config custom rules can evaluate SageMaker model deployment configurations against defined policies (e.g., requiring models to be from an approved registry), and AWS IAM policies can restrict who can create or update endpoints, together enforcing that only approved ML models are deployed. This combination provides both continuous compliance checking and access control, directly addressing the governance requirement.

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AWS CodePipeline and Amazon CodeGuru

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Amazon CloudWatch Events and AWS CloudTrail

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AWS Organizations and AWS Artifact

Question 11

Which AWS service provides managed foundation models from providers like Anthropic, Meta, and Stability AI through a single API?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from leading AI companies such as Anthropic (Claude), Meta (Llama), and Stability AI (Stable Diffusion) through a single, unified API. This allows developers to integrate and experiment with multiple FMs without managing underlying infrastructure or dealing with separate provider endpoints.

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AWS Lambda

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Amazon SageMaker

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Amazon Rekognition

Question 12

Refer to the exhibit. A data scientist ran a training job on Amazon SageMaker and it failed. Which action should the data scientist take FIRST to resolve the issue?

Accepted Answer

Use a different instance type that is available in the region. The error indicates that the requested instance type is not available in the current region due to capacity constraints. The first step is to switch to a different instance type that is available in the same region, as this is the quickest and most direct way to resolve the provisioning failure without requiring service limit increases or changing regions.

Answer

Request a service limit increase for the instance type

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Use a different AWS region

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Enable spot training

Question 13

A data scientist is prototyping a text summarization application using Amazon Bedrock. They want to quickly test different prompts and models without writing code. Which AWS service or feature should they use?

Accepted Answer

Bedrock Playground. Bedrock Playground is a no-code interface within Amazon Bedrock that allows users to experiment with different foundation models and prompts interactively. It is specifically designed for rapid prototyping without writing any code, making it the ideal choice for quickly testing text summarization prompts and models.

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AWS Cloud9

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Amazon SageMaker Studio

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AWS Lambda

Question 14

Which AWS service can be used to extract text and data from scanned documents such as invoices and receipts?

Accepted Answer

Amazon Textract. Amazon Textract is a machine learning service specifically designed to extract text, handwriting, and structured data (like tables and forms) from scanned documents. It goes beyond simple optical character recognition (OCR) by using ML models to understand the layout and relationships within documents, making it ideal for processing invoices and receipts.

Answer

Amazon Transcribe

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Amazon Comprehend

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Amazon Rekognition

Question 15

A financial services company needs to ensure that the machine learning models used for loan approval are explainable and meet regulatory compliance. Which AWS feature can help explain model predictions?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is the correct AWS service for explaining model predictions because it provides feature attribution and bias detection capabilities. It uses SHAP (SHapley Additive exPlanations) to generate explainability reports, which are essential for meeting regulatory compliance in financial services like loan approval.

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SageMaker Ground Truth

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SageMaker Automatic Model Tuning

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SageMaker Model Monitor