AIF-C01 Practice Test 33 — 15 Questions

Question 1

A data science team uses Amazon SageMaker to train models. To comply with SOC 2, they must ensure that access to training data is logged, that the data is encrypted at rest, and that model training jobs are isolated from each other. Which THREE actions should they take? (Choose three.)

Accepted Answer

Enable server-side encryption on the S3 bucket containing training data using SSE-KMS.. Option B is correct because enabling server-side encryption on the S3 bucket containing training data using SSE-KMS ensures data at rest is encrypted, which is a direct requirement for SOC 2 compliance. SSE-KMS provides envelope encryption with a customer-managed AWS KMS key, allowing fine-grained access control and audit trails for the encryption keys.

Answer

Enable Amazon Inspector to scan training instances for vulnerabilities.

Answer

Use SageMaker Debugger to monitor training jobs.

Question 2

During a security review, it is found that an Amazon SageMaker notebook instance has outbound internet access, which could lead to data exfiltration. The notebook must only access resources within the VPC. Which step should be taken to restrict internet access?

Accepted Answer

Configure the notebook instance in a VPC with no internet gateway or NAT device, and set the notebook's 'Direct Internet Access' option to 'Disabled'. Option C is correct because disabling 'Direct Internet Access' on a SageMaker notebook instance and placing it in a VPC without an internet gateway or NAT device ensures the notebook cannot reach the public internet. This configuration forces all traffic to stay within the VPC, preventing data exfiltration via outbound internet connections while still allowing access to VPC resources.

Answer

Modify the notebook instance's IAM role to deny s3:GetObject

Answer

Attach a security group that denies all outbound traffic to 0.0.0.0/0

Answer

Disable the SageMaker notebook instance's root volume encryption

Question 3

A company is using Amazon Comprehend to analyze customer feedback. They need to ensure that the documents are encrypted at rest. What should they do?

Accepted Answer

No action is needed; Amazon Comprehend automatically encrypts data at rest using AES-256. Amazon Comprehend automatically encrypts all data at rest using AES-256 encryption by default, with no additional configuration required. This encryption covers both the documents processed by the service and any models or artifacts stored internally. Therefore, no action is needed from the customer to enable encryption at rest.

Answer

Enable encryption using AWS KMS in the Comprehend console

Answer

Store documents in an encrypted S3 bucket and use a VPC endpoint

Answer

Use SSL/TLS for all API calls to Comprehend

Question 4

A company is using Amazon Bedrock to build a generative AI application. The company wants to prevent the model from generating toxic or harmful content while still allowing creative responses. Which feature should the company enable?

Accepted Answer

Amazon Bedrock Guardrails with content filters.. Amazon Bedrock Guardrails with content filters is the correct feature because it allows the company to define and enforce policies that block toxic or harmful content in model inputs and outputs, while still permitting creative responses within safe boundaries. This feature provides configurable thresholds for content categories like hate, insults, and sexual content, enabling precise control over model behavior without restricting overall creativity.

Answer

AWS Key Management Service (KMS) to encrypt model responses.

Answer

AWS Identity and Access Management (IAM) policies to restrict model output.

Answer

Amazon CloudWatch Logs to monitor and block harmful content.

Question 5

A company uses Amazon SageMaker Pipelines for MLOps. The security team requires that all pipeline steps use only approved Docker images from a private Amazon ECR repository, and that all pipeline artifacts are encrypted with a customer managed KMS key. Which THREE steps must the company configure to meet these requirements? (Choose three.)

Accepted Answer

Specify a KMS key in the pipeline definition for encrypting output artifacts.. Option A is correct because Amazon SageMaker Pipelines allows you to specify a KMS key in the pipeline definition to encrypt output artifacts at rest. This ensures that all artifacts generated by pipeline steps are encrypted using a customer managed KMS key, meeting the security team's encryption requirement.

Answer

Set an ECR lifecycle policy to delete untagged images older than 30 days.

Answer

Enable AWS Config rules to check for public ECR repositories.

Question 6

A financial services company is deploying a fraud detection model using Amazon SageMaker. They need to ensure that inference requests are encrypted in transit and that the model endpoint is only accessible from within a VPC. What is the MOST secure way to achieve this?

Accepted Answer

Deploy the model endpoint in a VPC with an interface VPC endpoint (AWS PrivateLink) and configure the model to require HTTPS. Option D is correct because it combines two critical security controls: deploying the SageMaker endpoint within a VPC using an interface VPC endpoint (AWS PrivateLink) ensures that all traffic stays within the AWS network and never traverses the public internet, while configuring the model to require HTTPS enforces encryption in transit. This architecture eliminates exposure to the internet and meets the requirement for encrypted inference requests and VPC-only access.

Answer

Enable public endpoint with TLS and restrict access using a VPN

Answer

Use SageMaker Model Monitor to detect anomalies in request data

Answer

Create a SageMaker endpoint in a private subnet without an internet gateway, and use AWS PrivateLink

Question 7

An administrator reviews a CloudTrail log entry for a CreateModel API call. Which security concern should they investigate?

Accepted Answer

The model data URL uses HTTP instead of HTTPS. Option B is correct because using HTTP instead of HTTPS for the model data URL exposes the data in transit to potential interception or tampering. CloudTrail logs record the URL as provided, and if it uses HTTP, the data transferred from that URL to SageMaker is unencrypted, violating security best practices for data in transit. This is a direct security concern that should be investigated and remediated by using HTTPS.

Answer

The model name is not encrypted in the log

Answer

The source IP address is an external IP

Answer

The execution role ARN is visible in the log

Question 8

A company needs to secure data used for training machine learning models in Amazon SageMaker. Which TWO actions should be taken to protect data at rest and in transit? (Choose TWO.)

Accepted Answer

Enable default encryption on S3 buckets using AWS KMS. Option B is correct because enabling default encryption on S3 buckets using AWS KMS ensures that all objects stored in the bucket are encrypted at rest. This is a fundamental security practice for protecting training data in SageMaker, as SageMaker reads data from S3. Using AWS KMS allows you to manage and audit encryption keys, providing fine-grained control over data protection.

Answer

Use AWS Certificate Manager (ACM) to generate certificates for all SageMaker endpoints

Answer

Disable root access on SageMaker notebook instances

Answer

Use SageMaker Model Registry to track model versions

Question 9

A company uses Amazon SageMaker to train a model on customer transaction data. The data is stored in an S3 bucket encrypted with SSE-KMS. The data scientists are using a SageMaker notebook instance to preprocess the data. The security team notices that the notebook instance can access the KMS key directly through the SageMaker console, even when the data scientists do not have explicit KMS permissions. What is the MOST likely reason for this behavior?

Accepted Answer

The SageMaker execution role attached to the notebook instance has permission to use the KMS key for the S3 bucket, and the SageMaker console uses that role to perform decryption operations on behalf of the user.. Option C is correct because the SageMaker notebook instance uses an execution IAM role that is assumed when performing operations. If that execution role has permissions to use the KMS key (e.g., kms:Decrypt, kms:GenerateDataKey) for the S3 bucket, the SageMaker console can leverage that role to decrypt data on behalf of the user, even if the user's own IAM identity lacks explicit KMS permissions. The console acts as a proxy, using the notebook's execution role to access the key.

Answer

The KMS key policy allows the SageMaker service to decrypt using the key, and the console displays this permission globally.

Answer

The S3 bucket has a bucket policy that grants s3:GetObject access using the SageMaker service principal, bypassing KMS permissions.

Answer

The SageMaker notebook instance is using an IAM role that has the AWS managed policy AmazonSageMakerFullAccess, which includes kms:Decrypt for all keys.

Question 10

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. Option D is correct because 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

Answer

Implement access logging for all API calls

Answer

Encrypt data at rest and in transit

Question 11

A company wants to use Amazon SageMaker to train a model on sensitive healthcare data. What is the MOST secure way to ensure that the training data is encrypted at rest and in transit, and that only authorized users can access the model artifacts?

Accepted Answer

Enable server-side encryption with AWS KMS managed keys for the S3 buckets, SageMaker notebook instances, and training jobs, and use IAM roles to limit access.. Option A is correct because it provides end-to-end encryption for sensitive healthcare data by enabling server-side encryption with AWS KMS managed keys for S3 buckets (data at rest), SageMaker notebook instances (data at rest and in transit via TLS), and training jobs (data at rest on ephemeral storage and in transit between services). IAM roles enforce least-privilege access to model artifacts, ensuring only authorized users can read or write them. This combination meets compliance requirements like HIPAA by encrypting data at rest (AES-256) and in transit (TLS 1.2+), while IAM policies control access.

Answer

Disable encryption for faster training, and rely on IAM policies to restrict access.

Answer

Use client-side encryption for all data, and store encryption keys in AWS Secrets Manager.

Answer

Use an AWS CloudHSM to store encryption keys and configure SageMaker to use it.

Question 12

A financial services company uses a foundation model for document analysis. They need to ensure the model does not output sensitive customer information from its training data. What is the most effective mitigation?

Accepted Answer

Implement output filtering using an external service. Output filtering using an external service is the most effective mitigation because it acts as a post-processing layer that can detect and redact sensitive customer information (e.g., PII, account numbers) before the model's response is returned to the user. This approach does not rely on the model's internal training or input modifications, which can be bypassed or incomplete. It provides a robust, policy-driven control that can be updated independently of the model.

Answer

Choose a model that has been fine-tuned on financial data

Answer

Apply data masking before sending input

Answer

Use a private endpoint

Question 13

A company wants to build a chatbot that responds to customer queries using a foundation model. They need low latency and want to avoid managing infrastructure. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from leading AI providers via a simple API, eliminating the need to manage underlying infrastructure. It is designed for building generative AI applications like chatbots with low latency, as it handles model hosting, scaling, and inference optimization automatically. This makes it the ideal choice for the company's requirement of low-latency responses without infrastructure management.

Answer

Amazon EC2

Answer

AWS Lambda

Answer

Amazon SageMaker

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.

Answer

AWS Config

Answer

AWS CloudTrail

Answer

AWS Organizations

Question 15

A media company is using Amazon Bedrock to generate marketing copy with a foundation model. They want to ensure the output adheres to brand voice guidelines (e.g., friendly, professional). Which prompt engineering strategy is most effective for this requirement?

Accepted Answer

Use a system prompt that explicitly describes the brand voice and expectations.. Option D is correct because Amazon Bedrock supports system prompts that set overarching context and behavioral guidelines for the model. By explicitly describing the brand voice (e.g., 'friendly, professional') in the system prompt, the model consistently applies these constraints across all user interactions, which is more effective than per-instruction tuning.

Answer

Provide five example outputs in the prompt that match the desired tone.

Answer

Include instructions like 'Do not use technical jargon' in every user prompt.

Answer

Set the temperature parameter to a low value (e.g., 0.1) to reduce randomness.