AIF-C01 Security, Compliance, and Governance for AI Solutions — 30 Questions

Question 1

A data scientist needs to restrict access to a specific Amazon SageMaker notebook instance so that only a designated IAM role can invoke the CreatePresignedNotebookInstanceUrl API. Which IAM policy element should be used to achieve this?

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

Accepted Answer

Add a Condition block in the IAM policy attached to the user's role. Amazon SageMaker notebook instances do not support resource-based policies. The correct way to restrict the CreatePresignedNotebookInstanceUrl API to a specific IAM role is to attach an identity-based policy to the user's role that includes a Condition block specifying the resource ARN of the notebook instance. This ensures only that role can generate presigned URLs for that instance.

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Modify the trust policy of the IAM role

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Place the notebook instance in a private VPC subnet

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Use a resource-based policy on the notebook instance

Question 2

A machine learning team needs to share a SageMaker notebook with a colleague from a different AWS account. The colleague should be able to open and run the notebook but not delete it. Which combination of actions should the team take?

Accepted Answer

Share the notebook via S3 with a bucket policy that grants read/write access to the colleague's account. SageMaker notebook instances do not support resource-based policies for cross-account access. To share a SageMaker notebook (the .ipynb file) with a colleague from a different AWS account, the notebook should be stored in an S3 bucket. A bucket policy can grant the colleague's account read access to the notebook file, and IAM roles can allow them to open and run it without delete permissions. Option A (Lake Formation) is used for sharing data lake resources, not typically for SageMaker notebooks directly. Option B is invalid because resource-based policies are not available for notebook instances. Option C grants too broad permissions.

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Use AWS Lake Formation to share the notebook

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Attach a resource-based policy to the notebook instance that grants the colleague's IAM role permissions to open and run, and deny delete

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Create an IAM role in the colleague's account with permissions to create and manage SageMaker notebooks

Question 3

A company is deploying a generative AI application using Amazon Bedrock and needs to ensure that the model's responses do not include any sensitive information. Which TWO Bedrock Guardrail configurations should be used together to meet this requirement? (Select TWO.)

Accepted Answer

PII redaction to redact or mask sensitive data. Amazon Bedrock Guardrails provide multiple mechanisms to control model outputs. PII redaction specifically identifies and removes or masks personally identifiable information, directly addressing the requirement to prevent sensitive data exposure. Content policies, on the other hand, filter outputs based on harmful content categories (e.g., hate, violence) and do not target sensitive information like PII. Therefore, only PII redaction (Option A) is needed to meet this specific requirement.

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Topic restriction to limit topics

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Word filter to block specific words

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Grounding check to verify responses are grounded in source documents

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Content policy to filter out harmful content

Question 4

A company uses Amazon SageMaker to train sensitive ML models. Which AWS service should they use to encrypt the training data and model artifacts at rest?

Accepted Answer

AWS Key Management Service (KMS). AWS Key Management Service (KMS) allows customers to create and manage encryption keys used to encrypt data at rest in SageMaker, including training data and model artifacts.

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AWS Secrets Manager

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AWS CloudHSM

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AWS Certificate Manager

Question 5

A data scientist needs to allow a foundation model in Amazon Bedrock to access a specific S3 bucket containing reference documents. The bucket is in a different AWS account. What is the MOST secure way to grant access?

Accepted Answer

Configure the S3 bucket policy to allow access from the Bedrock service role and add a Bedrock resource policy allowing the bucket. Cross-account access requires both the S3 bucket policy to grant the Bedrock service role and the Bedrock resource policy to allow the bucket, ensuring least privilege.

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Use AWS Lake Formation to grant cross-account access to the bucket

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Copy the S3 bucket to the same account as Bedrock

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Make the S3 bucket public and use a pre-signed URL

Question 6

A company uses Bedrock Guardrails to filter harmful content in a generative AI application. They need to prevent the model from discussing proprietary internal projects. Which Guardrail component should be configured?

Accepted Answer

Topic restrictions. Topic restrictions allow administrators to define denied topics; the model will not generate responses related to those topics.

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Content filters

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Grounding check

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Word filters

Question 7

A financial services firm needs to ensure that all calls to Amazon Bedrock APIs are logged for audit purposes. Which AWS service should they enable to capture API calls?

Accepted Answer

AWS CloudTrail. AWS CloudTrail records API activity in AWS accounts, including Bedrock API calls, providing audit logs.

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Amazon S3 server access logs

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Amazon CloudWatch Logs

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AWS Config

Question 8

A company wants to detect sensitive data such as PII in their training datasets stored in S3 before using them for model training. Which AWS service should they use?

Accepted Answer

Amazon Macie. Amazon Macie uses machine learning to discover and protect sensitive data in S3, including PII.

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Amazon Inspector

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AWS Shield

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Amazon GuardDuty

Question 9

A company uses SageMaker Clarify to detect bias in a deployed model. The monitoring must run automatically on a schedule. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor can be configured to run bias detection jobs on a schedule using Clarify's bias metrics.

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SageMaker Pipelines

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SageMaker Experiments

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SageMaker Data Wrangler

Question 10

An organization uses AWS Lake Formation to govern a data lake used for SageMaker training. They need to enforce row-level security so that different teams only see data relevant to their projects. Which Lake Formation feature should they use?

Accepted Answer

Row-level security. Lake Formation's row-level security uses filters to restrict access to specific rows based on conditions.

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Cell-level security

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Column-level security

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Tag-based access control

Question 11

A company wants to use Amazon Bedrock to generate responses grounded in their proprietary knowledge base. They need to minimize hallucinations and ensure responses are based on the provided documents. Which feature should they enable?

Accepted Answer

Grounding check. Grounding check in Bedrock Guardrails verifies that the model's response is supported by the source documents, reducing hallucinations.

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Topic restrictions

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Word filters

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Content filters

Question 12

A data scientist needs to restrict access to a SageMaker notebook instance to only the corporate network. Which configuration should they use?

Accepted Answer

Place the notebook instance in a VPC and configure security groups to allow only corporate IP ranges. VPC isolation places the notebook instance inside a VPC, and security groups can restrict inbound traffic to the corporate network IP range.

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Enable multi-factor authentication for the notebook

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Use an IAM policy to allow only corporate users

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Use a SageMaker lifecycle configuration to block external IPs

Question 13

A company needs to ensure that model inference endpoints in SageMaker are only accessible from a private subnet in their VPC, and no traffic goes over the public internet. Which network configuration should they use?

Accepted Answer

Use a VPC interface endpoint for SageMaker and disable public access. A VPC-only endpoint (interface endpoint) or private endpoint without public internet access ensures traffic stays within the VPC.

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Create a public endpoint and restrict access using IAM policies

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Use AWS PrivateLink for the endpoint, but keep public endpoint enabled

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Deploy the endpoint in a public subnet with a security group blocking all inbound traffic

Question 14

A company using Amazon Bedrock needs to redact personally identifiable information (PII) from user inputs before sending them to the foundation model. Which Bedrock Guardrails component should be configured?

Accepted Answer

Sensitive information filters. PII redaction in Guardrails automatically detects and redacts PII in user inputs or model responses.

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Content filters

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Topic restrictions

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Word filters

Question 15

A company wants to use a third-party foundation model in Bedrock and is concerned about the provider's data handling policies. Which action should they take to ensure their data is not used for model training by the provider?

Accepted Answer

Review the provider's data privacy policy and enable the opt-out option for training data use if available. AWS provides model access approval settings where customers can review and accept data handling policies; many providers offer opt-out options for training data use.

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Use Bedrock's content filters to block sensitive data

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Encrypt all data with KMS before sending to Bedrock

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Use a custom model hosted on SageMaker instead

Question 16

A company needs to govern the lifecycle of ML models, including versioning, monitoring for drift, and decommissioning outdated models. Which TWO services should they use? (Choose 2)

Accepted Answer

Amazon SageMaker Model Registry. SageMaker Model Registry handles versioning and model approval; SageMaker Model Monitor tracks drift and performance.

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AWS CloudTrail

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Amazon S3

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AWS CodePipeline

Question 17

A company wants to enforce strict data residency for training data used in SageMaker. The data must never leave a specific AWS Region. Which THREE actions should they take? (Choose 3)

Accepted Answer

Use KMS customer managed keys specific to the region. To enforce data residency, the company must use a VPC in the desired region, configure S3 bucket policies to block cross-region access, and use KMS keys regional to that region.

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Use AWS KMS multi-Region keys to encrypt data

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Enable cross-region replication for the S3 bucket

Question 18

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. Bedrock invocation logging supports S3 and CloudWatch Logs as destinations.

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Amazon DynamoDB

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AWS CloudTrail

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Amazon Kinesis Data Firehose

Question 19

A data scientist wants to restrict which IAM roles can invoke a specific Amazon Bedrock base model. Which AWS feature should they use?

Accepted Answer

Bedrock resource-based policy. Bedrock resource policies allow you to attach fine-grained permissions to a specific model, specifying which principals (IAM roles) can invoke it.

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S3 bucket policy on the model artifacts

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AWS KMS key policy for the encryption key

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AWS CloudTrail log delivery policy

Question 20

A healthcare company uses Amazon SageMaker to train a model on patient data. To meet HIPAA compliance, they must ensure training data is encrypted at rest and in transit. Additionally, the training job should not have internet access. Which combination of actions should the company take?

Accepted Answer

Specify a KMS key for SageMaker's VolumeKmsKeyId and configure the training job to run in a VPC without a public internet route. Using KMS encryption keys and configuring the training job to run in a VPC without a public internet route satisfies both encryption and network isolation requirements.

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Use AWS Certificate Manager (ACM) to issue a certificate for the training job and enable network isolation

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Enable SageMaker data capture and use a private VPC with an internet gateway

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Store training data in an encrypted S3 bucket and use a SageMaker notebook instance inside a VPC