AIF-C01 Practice Questions 2 — 50 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.

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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":"*"}

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{"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.

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JSON

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Image (JPEG/PNG)

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CSV

Question 11

A data scientist is evaluating different AWS services for building a machine learning pipeline. Which THREE components are part of Amazon SageMaker? (Select THREE.)

Accepted Answer

Notebook instances. Amazon SageMaker provides fully managed notebook instances that allow data scientists to spin up Jupyter notebooks for data exploration, preprocessing, and model development without managing underlying infrastructure. These instances come pre-installed with common ML frameworks and can be easily scaled.

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AWS Glue

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Amazon Athena

Question 12

Refer to the exhibit. A developer wants to ensure the notebook instance can access the internet to download packages. Which property configuration ensures this?

Accepted Answer

DirectInternetAccess: Enabled. Option A is correct because setting `DirectInternetAccess: Enabled` on an AWS SageMaker notebook instance allows it to access the internet through a VPC with a Network Address Translation (NAT) gateway or via the public internet if the instance is not in a VPC. This configuration is required to download packages from external repositories like PyPI or conda.

Answer

VolumeSizeInGB: 5

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InstanceType: ml.t2.medium

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The resource type AWS::SageMaker::NotebookInstance

Question 13

A data scientist wants to deploy a custom model built with TensorFlow to Amazon SageMaker for real-time inference. Which TWO steps are required? (Choose two.)

Accepted Answer

Upload the model artifacts to an S3 bucket. Option B is correct because SageMaker requires model artifacts (the trained model files) to be stored in an S3 bucket before they can be used for inference. When deploying a custom TensorFlow model, you must upload the saved model (e.g., in SavedModel format) to S3, and then SageMaker will download it to the inference container during endpoint creation.

Answer

Create an Amazon ECR repository for the inference container

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Submit a training job to SageMaker

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Convert the model to ONNX format

Question 14

A team has built a regression model to predict house prices. The RMSE is 50,000 on the test set. Which action is most appropriate to improve model performance?

Accepted Answer

Add more relevant features. Option C is correct because adding relevant features can capture more patterns and improve predictive accuracy. Using a different metric (A) does not improve the model. Removing outliers (B) may help if outliers exist, but adding features is generally a more systematic improvement. Feature scaling (D) helps some algorithms but may not be the primary issue.

Answer

Remove outliers from training data

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Apply feature scaling

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Use a different evaluation metric

Question 15

A data engineer is using Amazon SageMaker Data Wrangler to prepare tabular data for ML. Which THREE data transformations are natively supported? (Choose three.)

Accepted Answer

One-hot encoding for categorical features. Option A is correct because Amazon SageMaker Data Wrangler includes built-in support for one-hot encoding as a native transformation for categorical features. This transformation automatically creates binary columns for each category, which is essential for preparing tabular data for machine learning models that require numerical input.

Answer

Audio feature extraction

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Image resizing and normalization

Question 16

A data scientist needs to preprocess categorical data with high cardinality (e.g., zip code with 50,000 unique values). Which technique is most appropriate?

Accepted Answer

Target encoding. Option A is correct because target encoding uses the target variable to encode categories, reducing dimensionality while capturing predictive power. One-hot encoding (B) creates too many features. Label encoding (C) implies ordinal relationship which is unsuitable. Ordinal encoding (D) also implies order.

Answer

Label encoding

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Ordinal encoding

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One-hot encoding

Question 17

A company wants to deploy a real-time inference endpoint for a custom model on SageMaker. The model has high latency (100ms) and they need to handle variable traffic with spikes. Which deployment strategy is most cost-effective?

Accepted Answer

Deploy on a single SageMaker endpoint with automatic scaling. Option C is correct because a single SageMaker endpoint with automatic scaling allows the endpoint to dynamically adjust the number of instances based on traffic patterns, handling variable traffic and spikes cost-effectively. For a model with 100ms latency, automatic scaling can add instances during spikes and remove them during low traffic, ensuring you only pay for the compute resources you use while maintaining low inference latency.

Answer

Deploy on a SageMaker multi-model endpoint

Answer

Use batch transform

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Use a single large instance type

Question 18

A deployed model on an Amazon SageMaker endpoint is experiencing high inference latency (average 500ms) during peak hours. The model is a deep neural network with 10 million parameters. The endpoint uses a single ml.c5.xlarge instance. The company wants to reduce latency to under 200ms without retraining or changing the model architecture. Which action should they take?

Accepted Answer

Use SageMaker Neo to compile and optimize the model. SageMaker Neo compiles trained models into an optimized format for the target hardware, reducing inference latency without altering the model architecture. For a deep neural network with 10 million parameters on a CPU instance, Neo applies hardware-specific optimizations like operator fusion and memory layout tuning, which can significantly lower latency. This directly addresses the requirement to reduce latency from 500ms to under 200ms without retraining or changing the model.

Answer

Enable automatic scaling to add more instances

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Switch to a GPU-based instance type like ml.p2.xlarge

Answer

Deploy the model on a multi-model endpoint

Question 19

A data science team is using Amazon SageMaker to train multiple models with different hyperparameters. They want to track metrics, compare runs, and reproduce the best result. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct feature because it is specifically designed to track, organize, and compare machine learning training runs (trials) with different hyperparameters and metrics. It allows data scientists to log parameters, metrics, and artifacts for each run, compare results across runs, and retrieve the exact configuration needed to reproduce the best-performing model.

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SageMaker Model Registry

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SageMaker Debugger

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SageMaker Autopilot

Question 20

Which TWO of the following are best practices for data preprocessing in machine learning? (Select TWO.)

Accepted Answer

Use cross-validation to evaluate model performance. Cross-validation is a best practice for evaluating model performance because it provides a more robust estimate of how the model will generalize to unseen data by partitioning the data into multiple training and validation sets. This reduces the variance associated with a single train-test split and helps detect overfitting, making it a standard technique in machine learning workflows.

Answer

Always split data 80/20 for training and testing

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One-hot encoding for ordinal categories

Answer

Drop duplicate records only if they are manual entry errors