AIF-C01 Security, Compliance and Governance for AI Solutions Practice Test 4 — 15 Questions

Question 1

A machine learning engineer notices that a SageMaker training job failed due to insufficient permissions to access a KMS-encrypted S3 bucket. The training job's IAM role has S3 access permissions. What should be done to resolve the issue?

Accepted Answer

Add a kms:Decrypt permission to the IAM role for the specific KMS key used to encrypt the S3 bucket. The training job failed because the IAM role lacks permission to decrypt the KMS key used for S3 server-side encryption. Even with S3 access permissions, SageMaker cannot read encrypted objects without the kms:Decrypt action on the specific KMS key. Adding kms:Decrypt to the role's policy for that key resolves the issue.

Answer

Create a new KMS key and re-encrypt the data

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Assign the SageMakerFullAccess policy to the role

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Change the S3 bucket's default encryption to S3-managed keys (SSE-S3)

Question 2

A healthcare company needs to use Amazon SageMaker Ground Truth for data labeling. The data includes protected health information (PHI) that must remain in the US. Which configuration meets the compliance requirements?

Accepted Answer

Use a private workforce consisting of the company's employees and launch the labeling job in the us-east-1 region. Option B is correct because a private workforce consisting of the company's own employees ensures that PHI is never exposed to external workers, and launching the labeling job in us-east-1 keeps all data within the US, satisfying the data residency requirement. Amazon SageMaker Ground Truth allows you to restrict data access to a private workforce that you manage, and by selecting a US region, you ensure that data processing and storage remain within US borders.

Answer

Use a vendor-managed workforce and set up data encryption

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Use a public workforce (Mechanical Turk) and select the US East region

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Use a private workforce but launch the labeling job in the eu-west-1 region

Question 3

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. Option D is correct because 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.

Answer

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 4

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

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

Answer

Use SSL/TLS for all API calls to Comprehend

Question 5

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 6

An IAM policy allows creation of SageMaker training jobs only if they use a specific VPC security group. A user tries to create a training job without specifying that security group. What will happen?

Accepted Answer

The request will be denied with an AccessDenied error. Option D is correct because IAM policies are evaluated before any AWS API action is executed. If the policy includes a condition that requires a specific VPC security group for SageMaker training jobs, and the user's request does not include that security group, the condition is not met, resulting in an explicit deny (AccessDenied error). AWS IAM denies the request by default if the condition in a policy is not satisfied, regardless of whether the condition is marked as optional in the API.

Answer

The request will succeed but SageMaker will ignore the condition

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The request will succeed because the condition is optional

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The request will be denied because the training job resource ARN is invalid

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

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The source IP address is an external IP

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The execution role ARN is visible in the log

Question 8

A data scientist sets up a Model Monitoring schedule for data quality. What is a potential security issue with this configuration?

Accepted Answer

The monitoring job uses a single role for both training and monitoring, violating least privilege. Option A is correct because using a single AWS Identity and Access Management (IAM) role for both the training job and the monitoring job violates the principle of least privilege. The training role typically requires broader permissions (e.g., access to training datasets, SageMaker full access), while the monitoring role only needs read-only access to the endpoint and write access to the monitoring output location. Sharing a single role increases the blast radius if the monitoring job is compromised, as an attacker could leverage the elevated training permissions to modify or exfiltrate data.

Answer

The schedule runs every hour, which may generate too many logs

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The monitoring job uses the same endpoint as the production model

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The output is stored in an S3 bucket with no encryption

Question 9

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 10

A company uses Amazon SageMaker to build and deploy models. They want to enforce compliance that all model endpoints are encrypted in transit and use least privilege access. Which THREE steps should they take? (Choose THREE.)

Accepted Answer

Configure the SageMaker endpoint to use a custom SSL certificate via AWS Certificate Manager. Option A is correct because configuring a SageMaker endpoint to use a custom SSL certificate from AWS Certificate Manager (ACM) ensures that all data transmitted between clients and the endpoint is encrypted in transit using TLS. This enforces the compliance requirement for encryption in transit by replacing the default SageMaker certificate with a customer-managed certificate, which can be validated and rotated as needed.

Answer

Enable AWS CloudTrail to log all endpoint invocations

Answer

Disable root access on the SageMaker notebook instances

Question 11

Which TWO AWS services can be used to monitor and detect security anomalies in Amazon SageMaker model inference data? (Choose TWO.)

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is specifically designed to detect deviations in model quality, such as data drift and feature attribution drift, by continuously monitoring inference data against a baseline. Amazon CloudWatch Logs can be used to capture and analyze inference request logs, enabling custom anomaly detection through log-based metrics and alarms. Together, they provide a comprehensive approach to monitoring security anomalies in SageMaker model inference data.

Answer

Amazon Macie

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AWS CloudTrail

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Amazon CodeGuru Security

Question 12

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 13

A company deploys an Amazon Bedrock agent that uses a knowledge base with sensitive financial documents. The security team requires that all data retrieval queries be logged for auditing, and that the LLM responses do not contain any personally identifiable information (PII). What combination of services should the company use?

Accepted Answer

Enable Amazon CloudWatch Logs for the Bedrock agent and use Amazon Comprehend to redact PII from responses.. Option B is correct because Amazon CloudWatch Logs can capture query logs, and Amazon Comprehend (or Bedrock Guardrails) can detect and redact PII. Option A is wrong because GuardDuty is for threat detection, not PII redaction. Option C is wrong because AWS Config is for resource compliance, not logging queries. Option D is wrong because Macie is for data discovery in S3, not real-time PII redaction in responses.

Answer

Enable AWS CloudTrail for API logging and use Amazon GuardDuty to detect PII in responses.

Answer

Use AWS Config to monitor Bedrock resource configurations and apply an IAM policy to restrict PII.

Answer

Use Amazon S3 server access logs for the knowledge base and enable Amazon Macie to redact PII.

Question 14

A financial institution uses Amazon SageMaker to host a model for credit scoring. The model was trained on data that includes demographic attributes. During a routine audit, the compliance team finds that the model produces significantly different approval rates for applicants of different ethnicities, even when credit profiles are similar. The institution must continue using the model but needs to ensure compliance with fair lending laws. What should the company do FIRST?

Accepted Answer

Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.. Amazon SageMaker Clarify is the correct first step because it provides built-in bias detection and explainability for machine learning models. Before taking any corrective action, the company must first quantify and understand the nature and extent of the bias using SageMaker Clarify's bias metrics (e.g., Difference in Positive Proportions, Disparate Impact). This diagnostic report is essential for compliance documentation and for determining whether the bias is due to the model, the data, or the threshold, thereby guiding any subsequent remediation steps.

Answer

Adjust the decision threshold to equalize approval rates across groups.

Answer

Document the disparity in a compliance report and continue using the model.

Answer

Replace the model with a simpler explainable model to eliminate bias.

Question 15

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.

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Amazon CloudWatch Logs to monitor and block harmful content.