AIF-C01 Practice Test 6 — 10 Questions

Question 1

A healthcare startup deploys a model to predict patient readmission risk using Amazon SageMaker. After deployment, the model shows higher false-positive rates for a specific age group. What is the most responsible first step?

Accepted Answer

Use Amazon SageMaker Clarify to detect bias in predictions. Amazon SageMaker Clarify is purpose-built for detecting bias in ML models and data. It provides bias metrics (e.g., Difference in Positive Proportions in Predicted Labels, Disparate Impact) that can quantify whether the model's predictions are systematically skewed against a specific age group. This is the most responsible first step because it objectively measures the bias before any corrective action is taken.

Answer

Increase the prediction threshold for the affected group

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Retrain the model with more data from the affected group

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Immediately retire the model to prevent harm

Question 2

A retail company uses a recommendation system that occasionally suggests inappropriate products to minors. Which responsible AI practice should be applied?

Accepted Answer

Implement human review of flagged recommendations. The correct practice is to implement human review of flagged recommendations. This aligns with the responsible AI principle of accountability, where automated systems must have oversight mechanisms to catch and correct inappropriate outputs, especially when minors are involved. Human-in-the-loop (HITL) validation ensures that edge cases or subtle context (e.g., age-inappropriate product suggestions) are caught before they reach end users, rather than relying solely on automated filters or feedback loops.

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Rely solely on user feedback to improve

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Disable the recommendation system entirely

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Increase the volume of training data

Question 3

A company develops a chatbot using Amazon Lex. To ensure transparency, what should the chatbot do when it cannot answer a question?

Accepted Answer

Clearly state that it cannot answer and offer alternatives. Option C is correct because responsible AI guidelines, including those from AWS for Amazon Lex, require that when a chatbot cannot answer a question, it should clearly state its inability and offer alternatives (e.g., rephrasing the query or providing related topics). This maintains transparency and user trust, aligning with the 'Explainability' principle under the Guidelines for Responsible AI.

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Remain silent and wait for the next input

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Provide a random answer from a predefined list

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Automatically escalate all unanswered questions to a human

Question 4

Which TWO actions are most aligned with responsible AI practices when deploying a model that makes decisions affecting individuals? (Choose 2)

Accepted Answer

Continuously monitor the model for fairness metrics. Options A and C are correct. Option A: Providing explanations supports transparency. Option C: Regular monitoring detects bias drift. Option B is wrong because teams should include diverse perspectives. Option D is wrong because using the most complex model may harm explainability. Option E is wrong because more data alone does not ensure fairness.

Answer

Collect as much data as possible without quality checks

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Ensure the development team is homogeneous to avoid conflicts

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Use the most complex model available for maximum accuracy

Question 5

A company uses Amazon Rekognition to analyze images stored in an S3 bucket. The security team requires that all image analysis be logged to AWS CloudTrail for auditing. What is the minimum configuration needed to meet this requirement?

Accepted Answer

Enable CloudTrail data events for the S3 bucket to capture GetObject API calls. Option D is correct because CloudTrail data events capture S3 object-level API operations such as GetObject, which is the API call made by Amazon Rekognition when it retrieves images from the S3 bucket for analysis. By enabling data events for the S3 bucket, every GetObject request is logged to CloudTrail, providing the audit trail the security team requires. Management events alone do not capture object-level operations, and S3 server access logs are not integrated with CloudTrail for auditing.

Answer

Enable Rekognition logging in the AWS Management Console

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Enable CloudTrail management events for the S3 bucket

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Enable S3 server access logs on the bucket

Question 6

A data scientist needs to grant an IAM user access to a specific Amazon SageMaker notebook instance. The user should only be able to start and stop the notebook instance, but not delete it. Which IAM policy statement should be used?

Accepted Answer

{"Effect":"Allow","Action":["sagemaker:StartNotebookInstance","sagemaker:StopNotebookInstance"],"Resource":"arn:aws:sagemaker:us-east-1:123456789012:notebook-instance/MyNotebook"}. Option B is correct because it uses the specific actions `sagemaker:StartNotebookInstance` and `sagemaker:StopNotebookInstance` with a resource ARN that targets only the intended notebook instance. This grants the least privilege required to start and stop the instance while explicitly preventing deletion, as no delete action is included. The resource ARN restricts the policy to a single notebook instance, ensuring the user cannot affect other resources.

Answer

{"Effect":"Allow","Action":["sagemaker:Start*","sagemaker:Stop*"],"Resource":"*"}

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{"Effect":"Allow","Action":"sagemaker:*","Resource":"*"}

Answer

{"Effect":"Allow","Action":"sagemaker:*","Resource":"arn:aws:sagemaker:us-east-1:123456789012:notebook-instance/MyNotebook"}

Question 7

A company uses Amazon SageMaker to host a real-time inference endpoint. The model was trained on sensitive data, and the company wants to ensure that the data sent to the endpoint is encrypted in transit. Additionally, the company wants to restrict access to the endpoint to only traffic originating from a specific VPC. Which configuration meets these requirements?

Accepted Answer

Create the SageMaker endpoint in a VPC, associate a security group that allows inbound HTTPS from the VPC CIDR, and configure the endpoint to use HTTPS. Option A is correct because creating the SageMaker endpoint within a VPC and associating a security group that restricts inbound HTTPS traffic to the VPC CIDR ensures that only traffic from within that VPC can reach the endpoint. Configuring the endpoint to use HTTPS encrypts data in transit using TLS, meeting both the encryption and VPC-only access requirements.

Answer

Configure the SageMaker endpoint to use mutual TLS (mTLS) with client certificates

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Place the SageMaker endpoint behind an Amazon CloudFront distribution with an origin access identity

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Use AWS STS to generate temporary credentials and require the client to sign requests with them

Question 8

A company is using Amazon Rekognition to detect objects in images stored in an S3 bucket. The company needs to ensure that the Rekognition service has permission to read images from the S3 bucket. Which TWO methods can achieve this? (Choose TWO.)

Accepted Answer

Attach an IAM role to the Lambda function that calls Rekognition, with an IAM policy granting s3:GetObject on the bucket. Option A is correct because when a Lambda function calls Amazon Rekognition, the function needs an IAM role with a policy that grants s3:GetObject permission on the source bucket. This allows the Lambda function to read the images and pass them to Rekognition for analysis. Option E is correct because you can attach a bucket policy that explicitly grants the Rekognition service principal (rekognition.amazonaws.com) permission to read objects, enabling Rekognition to directly access the S3 bucket when invoked.

Answer

Create an IAM role for Rekognition to assume and attach it to the Rekognition service

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Configure the S3 bucket with a bucket ACL that grants read access to the Rekognition service

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Attach a resource-based policy to the Rekognition collection allowing S3 access

Question 9

A data scientist is preparing data for a machine learning model. What is the purpose of splitting the data into training, validation, and test sets?

Accepted Answer

To evaluate model generalization. The test set is used to evaluate the final model's generalization performance on unseen data. The validation set is for hyperparameter tuning during development. Training set is for fitting the model.

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To tune hyperparameters

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To balance class distributions

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To prevent overfitting during training

Question 10

An ML engineer wants to store training data in a format optimized for linear data scanning and columnar access in SageMaker. Which format is most appropriate?

Accepted Answer

Parquet. Parquet is a columnar storage format optimized for both linear data scanning and columnar access, making it ideal for training data in SageMaker. It reduces I/O by storing data by columns rather than rows, enabling efficient retrieval of specific features during model training.

Answer

JSON

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Image (JPEG/PNG)

Answer

CSV