AIF-C01 Practice Test 36 — 15 Questions

Question 1

A large e-commerce company uses a recommendation system to suggest products to customers. Recently, a data scientist noticed that the model's recommendations for high-value luxury items are predominantly shown to users in affluent zip codes, while users in less affluent areas rarely see these items, even if they have searched for them. The company is concerned about fairness and wants to ensure all customers have equal access to recommendations regardless of location. The current model uses collaborative filtering on historical purchase data. The team needs to modify the system without sacrificing overall recommendation accuracy. Which action best addresses the fairness concern while maintaining performance?

Accepted Answer

Remove zip code and any income-correlated features from the training data. The most effective approach is to ensure the model does not use zip code or any feature correlated with income as a direct or indirect input. This removes the proxy for socioeconomic status. Simply equalizing recommendation frequency artificially may hurt relevance. Personalizing based on search history is already being done but zip code bias remains. Adding more training data may not help if the bias is in the features.

Answer

Randomly show luxury recommendations to a subset of users regardless of their behavior

Answer

Add more training data from less affluent areas to balance the dataset

Answer

Implement a separate recommendation pipeline for luxury items based only on search history

Question 2

A hospital uses an AI system to prioritize patients for organ transplant based on predicted survival rates. The system was trained on historical data that includes socioeconomic factors. A review reveals that the system systematically assigns lower priority to patients from lower-income neighborhoods, even when medical urgency is similar. The hospital's ethics board demands an immediate remedy. The data science team is small and must act quickly. What should the hospital do to address this fairness issue most effectively?

Accepted Answer

Retrain the model with only medically relevant features, after removing socioeconomic factors and correlated proxies. The best course is to retrain the model using only medically relevant features, removing socioeconomic factors and correlated proxies. This directly addresses the source of bias. Adding a penalty for low-income patients is artificial and may not reflect medical reality. Relying solely on human review delays the issue and introduces potential inconsistency. Using a different model without data changes may not eliminate bias.

Answer

Discontinue the AI system and have all prioritization done by a human committee

Answer

Apply a re-weighting penalty to boost priority for low-income patients

Answer

Use a different model type, such as a random forest instead of gradient boosting, on the same data

Question 3

A company is building a chatbot to answer customer queries using Amazon Lex. The development team has created a large dataset of customer interactions and intends to use Amazon SageMaker to train a custom machine learning model for natural language understanding (NLU). The team wants to integrate the trained model with Amazon Lex to handle intents and slots. The team has limited experience with SageMaker and wants to minimize operational overhead. Which solution should the team use?

Accepted Answer

Use SageMaker Autopilot to automatically build and train the best model, then deploy to a SageMaker endpoint and use Lambda to invoke it for Lex integration.. Option D is correct because SageMaker Autopilot automates model building, tuning, and deployment, reducing the need for manual intervention and expertise. Option A requires manual algorithm selection and tuning. Option B uses Amazon Comprehend, which provides general-purpose NLP but does not allow for custom NLU model training. Option C is not supported because Amazon Lex does not directly invoke custom Docker containers; integration is typically done via Lambda.

Answer

Encapsulate the custom model in a Docker container, push it to Amazon ECR, and create a custom machine learning resource in Amazon Lex to invoke the container directly.

Answer

Train a custom model in SageMaker using a built-in algorithm like BlazingText, then deploy it to a SageMaker endpoint and integrate with Lex via a AWS Lambda function that calls the endpoint.

Answer

Use Amazon Comprehend to perform sentiment analysis and entity recognition, then map the results to Lex intents using Lambda.

Question 4

A healthcare company is developing a machine learning model using Amazon SageMaker to process protected health information (PHI). They have strict security requirements to comply with HIPAA. The company has implemented the following measures: All training data is stored in an S3 bucket with server-side encryption using AWS KMS. The data is accessed exclusively through Amazon SageMaker notebooks running in a private VPC with no internet access. The VPC has a VPC endpoint for S3 to ensure traffic stays within the AWS network. Despite these measures, a recent security audit discovered that the S3 bucket containing the training data is accessible from an IP address outside the company's network. Upon investigation, it was found that the bucket policy allowed access from any IP address due to a misconfigured bucket policy. Which corrective action should the company take to prevent this issue from recurring?

Accepted Answer

Add a condition in the bucket policy to restrict access to the VPC endpoint ID.. Option B is correct because adding a condition to the bucket policy that restricts access to the VPC endpoint ID (aws:SourceVpce) ensures that only traffic from the VPC endpoint can access the bucket, preventing external IP access. Option A is incorrect because removing the bucket policy entirely would still allow access if IAM roles are not properly restricted. Option C is incorrect because disabling Block Public Access would increase risk. Option D is incorrect because moving data to EBS is not scalable and does not address the underlying policy issue.

Answer

Move the training data to an EBS volume attached to the SageMaker notebook.

Answer

Remove the bucket policy and rely solely on IAM policies to control access.

Answer

Disable S3 Block Public Access settings to allow more granular control.

Question 5

A company operates a customer support chatbot that uses Amazon Bedrock with a knowledge base sourced from an S3 bucket containing frequently updated product documentation. The knowledge base uses OpenSearch Serverless as the vector store and is configured to sync daily. The chatbot uses the RetrieveAndGenerate API with a custom Lambda function that applies a system prompt instructing the model to base answers solely on the retrieved context. After a major update to the product documentation, the IT team verifies that the data source sync completed successfully and the new chunks are present in the OpenSearch index. However, the chatbot continues to respond with outdated information. Further investigation reveals that the Lambda function includes a response caching mechanism using Amazon ElastiCache for Redis with a Time-To-Live (TTL) of 24 hours. The cache key is based on the user query. The team notes that no cache invalidation is performed after documentation updates. What is the most likely cause of the outdated responses?

Accepted Answer

The ElastiCache cache is returning stale cached responses that contain the old information.. Since the data source sync succeeded and the index contains new chunks, the retrieval should be able to access the latest data. However, the Lambda function caches responses keyed by query. With a 24-hour TTL and no invalidation, the cache returns stale responses containing the old safety information. Clearing the cache or reducing TTL would resolve the issue. The other options are less likely: low max results might cause missing new chunks but would not consistently return old info; the embedding model is not retrained per sync; IAM permissions would affect sync, not retrieval.

Answer

The 'maximum results' parameter in the RetrieveAndGenerate API is set to a value too low to retrieve the new chunks.

Answer

The embedding model used by the knowledge base has not been retrained on the new documentation.

Answer

The IAM role for the Lambda function lacks permissions to access the new S3 objects.

Question 6

A healthcare company uses Amazon SageMaker to train a model that predicts patient readmission risk based on electronic health records (EHRs) stored in Amazon HealthLake. The training dataset contains 2 million records from the past three years, with a significant gender imbalance: 70% male and 30% female. The model achieved high overall accuracy, but further analysis using SageMaker Clarify revealed that the precision for female patients is 0.65 while for male patients it is 0.88. Additionally, the model's false positive rate for female patients is significantly higher. The company must comply with healthcare regulations that require fairness and non-discrimination. The data science team has already used SageMaker Data Wrangler for initial preprocessing and SageMaker Clarify for bias detection. They need to take immediate action to mitigate the bias before deploying to production. Which course of action should the team take?

Accepted Answer

Use SageMaker Clarify's bias mitigation feature to apply reweighing techniques and retrain the model with adjusted sample weights.. The correct answer is to use SageMaker Clarify's built-in bias mitigation technique (reweighing) as it directly addresses the disparity by adjusting sample weights during training. Option A: Model Monitor is for monitoring drift, not mitigation. Option B: SHAP values explain predictions but do not change model behavior. Option C: SMOTE addresses class imbalance but not fairness in terms of group accuracy disparity; it may even worsen bias. Therefore, D is the best choice.

Answer

Use SageMaker Clarify to generate SHAP values and adjust the model's feature importance by removing biased features.

Answer

Use SMOTE (Synthetic Minority Oversampling Technique) to balance the training dataset before retraining.

Answer

Use SageMaker Model Monitor to detect feature drift and automatically retrain the model with updated data.

Question 7

A retail company uses a machine learning model to forecast daily product demand. The model is a time series model that uses historical sales data. The model has been performing well, but recently the forecasts have been consistently too low, leading to stockouts. The data scientist notices that the model was trained on data up to last year, and the company has since launched a successful marketing campaign that increased sales by 20%. The data scientist needs to update the model to reflect the new sales patterns. Which approach should the data scientist take?

Accepted Answer

Retrain the model on the most recent data that includes the sales from the marketing campaign.. Option D is correct because retraining the model on the most recent data that includes the sales from the marketing campaign allows the model to learn the new underlying pattern in the time series. Since the model is a time series model, it relies on historical patterns to make forecasts; retraining on data that captures the 20% sales lift ensures the model adapts to the new demand level, reducing the persistent underforecasting.

Answer

Add a feature for the marketing campaign and continue using the old model.

Answer

Switch to a different model type, such as ARIMA, without retraining.

Answer

Multiply the model's predictions by 1.2 to account for the marketing campaign.

Question 8

A media company is using Amazon Bedrock to generate captions for images. They have a batch processing pipeline that sends thousands of images daily to the Bedrock API using the Titan Image Generator G1 model. Recently, they started receiving ThrottlingException errors during peak hours. The team needs to process all images within 24 hours without changing the model or the application code. The current account has a default quota of 10 requests per second (RPS) for the Titan model in us-east-1. The team estimates they need 50 RPS during peak hours. They have already implemented exponential backoff in the client, but the errors persist. What is the MOST effective solution to resolve the throttling issue?

Accepted Answer

Request a service quota increase for the InvokeModel API for the Titan model in us-east-1. The team has already implemented exponential backoff, but the errors persist because their current quota of 10 RPS is insufficient for the required 50 RPS. Requesting a service quota increase for the InvokeModel API for the Titan Image Generator G1 model in us-east-1 directly addresses the root cause by raising the throughput limit, allowing the existing application code and model to handle the peak load without any architectural changes.

Answer

Use Amazon SageMaker batch transform to process images offline

Answer

Distribute the requests across multiple AWS Regions

Answer

Switch to a different foundation model that has a higher default quota

Question 9

A financial services company is deploying a generative AI model on Amazon SageMaker for real-time fraud detection. The model, a fine-tuned Llama 2 7B, must respond to transaction requests within 500 milliseconds. The team has deployed the model using a SageMaker real-time endpoint with a single ml.g5.2xlarge instance. During load testing, the endpoint achieves an average latency of 450 ms at 10 requests per second (RPS), but the latency spikes to over 2 seconds at 20 RPS. The team needs to maintain sub-500 ms latency at up to 50 RPS. The model is too large to fit on a single GPU, so they are using CPU instances. They considered using a larger instance type but want to minimize cost. What should the team do to meet the latency requirement cost-effectively?

Accepted Answer

Use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling. Option C is correct because a SageMaker multi-model endpoint (MME) allows multiple model replicas to be hosted on a fleet of instances, enabling horizontal scaling to handle increased throughput. By using multiple ml.g5.xlarge instances with auto scaling, the team can distribute the 50 RPS load across several instances, keeping per-instance latency low while minimizing cost compared to a single larger instance. This approach also leverages the fact that the model is too large for a single GPU but can be efficiently served on CPU instances with proper load distribution.

Answer

Upgrade to a single ml.g5.4xlarge instance

Answer

Attach an Amazon Elastic Inference accelerator to the existing instance

Answer

Use SageMaker Serverless Inference to automatically scale

Question 10

A healthcare organization is developing a clinical decision support system using Amazon Bedrock with a large language model (LLM) to analyze patient symptoms and suggest potential diagnoses. The system must comply with HIPAA and internal responsible AI guidelines. During testing, the model occasionally generates diagnoses that are inconsistent with established medical guidelines and shows a tendency to recommend more aggressive treatments for patients from certain demographic groups. The team has already implemented data encryption, access controls, and basic content filtering. They need to further reduce biased and unsafe outputs without delaying the deployment timeline. What should the team do next?

Accepted Answer

Fine-tune the model using a curated dataset of anonymized patient records that is balanced across demographic groups and aligned with clinical guidelines.. Option C is correct because fine-tuning the model with a balanced, curated dataset directly addresses both the bias and clinical accuracy issues at the model level, which is the most effective approach for reducing biased and unsafe outputs without delaying deployment. This method adjusts the model's internal weights to align with established medical guidelines and demographic fairness, rather than relying on post-processing filters or logging that do not fix the root cause. Since the team has already implemented basic content filtering, fine-tuning provides a targeted, efficient solution that can be completed within a reasonable timeline.

Answer

Increase the logging of all model inputs and outputs to Amazon CloudWatch and set up alarms for any mentions of protected attributes.

Answer

Replace the current LLM with a different pre-trained model that has been benchmarked for lower bias on medical datasets.

Answer

Apply stronger content filtering rules using Amazon Comprehend Medical to block any diagnosis that contains demographic-related terms.

Question 11

A healthcare company is using Amazon SageMaker to train and deploy a model that predicts patient readmission risk. The model uses sensitive protected health information (PHI). The company must ensure that data is encrypted at rest and in transit, and that access to the model endpoint is restricted to authorized applications only. The security team has configured AWS KMS customer managed keys for encryption, and IAM roles for SageMaker execution. However, during a security audit, it was discovered that the model endpoint is accessible from the internet and that the data used for training was stored in an S3 bucket with default encryption enabled. The compliance team requires that all PHI data be encrypted with a key that is rotated annually, and that no public access is allowed to the endpoint or training data. Which combination of actions should the ML engineer take to remediate these issues?

Accepted Answer

Enable S3 bucket encryption with a customer managed KMS key, disable public access on the SageMaker endpoint by deploying it in a VPC, and configure the KMS key to rotate annually.. Option D is correct because it addresses all compliance requirements: enabling S3 bucket encryption with a customer managed KMS key ensures PHI is encrypted at rest with a key that can be rotated annually, deploying the SageMaker endpoint in a VPC removes public internet access, and configuring annual KMS key rotation satisfies the rotation policy. This combination ensures encryption at rest and in transit (via VPC), restricts endpoint access to authorized applications only, and meets the key rotation requirement.

Answer

Use a SageMaker notebook instance with a lifecycle configuration to encrypt data with a customer managed KMS key, and restrict endpoint access using an IAM policy.

Answer

Enable S3 bucket encryption with SSE-S3, attach a bucket policy denying public access, and use an AWS Lambda function to rotate the S3 bucket key every year.

Answer

Apply SSE-KMS with an AWS managed key to the S3 bucket, and use a Lambda function to rotate the key every year. Disable public access to the endpoint using a VPC endpoint.

Question 12

A financial institution is deploying a fraud detection model using Amazon SageMaker. The model must be able to handle sudden spikes in inference requests during promotional events while keeping costs low. The team wants to use a serverless architecture to avoid provisioning idle capacity and to scale automatically from zero. However, the inference latency requirement is under 5 seconds for each request. Which SageMaker inference option should they choose?

Accepted Answer

Use Amazon SageMaker Serverless Inference. Amazon SageMaker Serverless Inference is the correct choice because it automatically scales from zero to handle sudden spikes in inference requests, aligning with the requirement to avoid provisioning idle capacity. It also meets the sub-5-second latency requirement for fraud detection, as it is designed for low-latency, on-demand inference without managing underlying infrastructure.

Answer

Use Amazon SageMaker Multi-Model Endpoints

Answer

Use Amazon SageMaker real-time endpoints with auto-scaling

Answer

Use Amazon SageMaker Asynchronous Inference

Question 13

A company is using Amazon SageMaker Ground Truth to create labeled datasets for a computer vision model. The dataset contains images of people in public places. The company must comply with data privacy regulations that require explicit consent for using images of individuals. The company has a privacy team that reviews the images and provides consent lists. The ML team suspects that some images in the dataset might include individuals who have not consented. The company wants to ensure that only those images with consent are used for training. What should the company do?

Accepted Answer

Create an Amazon Simple Workflow Service (SWF) workflow that cross-references image metadata with the consent list, and update the Ground Truth manifest to include only approved images.. Option C is correct because it uses Amazon Simple Workflow Service (SWF) to orchestrate a cross-referencing workflow between image metadata and the consent list, then updates the SageMaker Ground Truth manifest to include only approved images. This ensures that only images with explicit consent are used for training, directly addressing the data privacy compliance requirement without altering or mislabeling the data.

Answer

Apply a blur filter to all faces in the dataset using Amazon Rekognition before labeling.

Answer

Use Amazon Rekognition to detect faces in all images and re-label those without consent as invalid.

Answer

Use Amazon SageMaker Clarify to detect bias in the training data and exclude images of people.

Question 14

A healthcare company is deploying a model to predict patient readmission risk using Amazon SageMaker. The model processes protected health information (PHI). The company must adhere to HIPAA regulations, which require that all PHI is encrypted at rest and in transit, and that access to the data is logged and audited. The data is stored in an S3 bucket encrypted with SSE-S3. The SageMaker training job uses a custom Docker container that reads data from the S3 bucket and writes model artifacts back to another S3 bucket. The security team notices that the model artifacts in the output bucket are not encrypted. Also, the training logs in CloudWatch Logs contain PHI because the training script logs patient features. The company must fix these issues without delaying the deployment. What should the company do?

Accepted Answer

Modify the training container to avoid logging PHI, and configure the output S3 bucket to use SSE-KMS encryption.. Option C is correct because it directly addresses both issues: modifying the training container to avoid logging PHI ensures CloudWatch Logs no longer contain protected health information, and configuring the output S3 bucket to use SSE-KMS encryption ensures model artifacts are encrypted at rest. SSE-KMS is preferred over SSE-S3 for HIPAA compliance because it provides additional controls like key rotation and audit trails. This approach fixes the problems without delaying deployment, as it requires only code and configuration changes.

Answer

Implement client-side encryption for the output bucket, and update the training script to mask PHI before logging.

Answer

Change the output S3 bucket to use SSE-KMS encryption, and enable server access logs for the bucket.

Answer

Configure the training job to use a VPC with private subnets, and enable data encryption in transit using TLS.

Question 15

A marketing agency wants to analyze customer feedback from social media posts to gauge sentiment. They have no labeled data and limited ML expertise. The team needs a managed service that provides pre-trained models for sentiment analysis without requiring them to train or manage infrastructure. They also need to process text in multiple languages. Which AWS service should they use?

Accepted Answer

Use Amazon Comprehend with its default sentiment analysis model. Amazon Comprehend is a fully managed natural language processing (NLP) service that provides pre-trained models for sentiment analysis, key phrase extraction, and language detection. It requires no labeled data, no model training, and no infrastructure management, making it ideal for teams with limited ML expertise. Comprehend natively supports multiple languages, including Spanish, French, German, and many others, directly addressing the requirement to process text in multiple languages.

Answer

Use Amazon SageMaker to train a custom sentiment analysis model

Answer

Use AWS Glue to build a custom NLP pipeline

Answer

Use Amazon Rekognition for text analysis