Amazon Web Services · Free Practice Questions · Last reviewed May 2026
30real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
28% of exam · 6 sample questions below
A healthcare company is using Amazon Bedrock to summarize patient notes. The compliance team requires that no patient data is used to improve the underlying foundation model. Which configuration should the team choose?
Enable data encryption in transit and at rest.
Use a different foundation model from a different provider.
Disable model training data logging in the AWS console.
This setting prevents prompts and completions from being used for model improvement.
Configure a VPC endpoint for Amazon Bedrock.
A marketing firm uses Amazon Bedrock to generate ad copy. They notice that the generated text often includes factual inaccuracies about their products. Which technique would most effectively reduce these inaccuracies?
Implement Retrieval-Augmented Generation (RAG) with a product knowledge base.
RAG enables the model to retrieve and cite authoritative information, reducing hallucinations.
Use longer, more detailed prompts.
Increase the temperature parameter to 0.9.
Fine-tune the model on a dataset of previous ad copies.
A developer is using Amazon Bedrock to build a chatbot that answers customer queries. The chatbot must only respond based on the provided company documentation. Which approach best meets this requirement?
Use prompt engineering to instruct the model to only use documentation.
Use a RAG architecture with the company documentation as the knowledge base.
RAG ensures responses are based on retrieved documents.
Fine-tune a foundation model on the company documentation.
Use a text classification model to filter responses.
A financial services company is deploying a foundation model to analyze customer sentiment from call transcripts. The model outputs must be consistent and deterministic for auditing purposes. Which parameter configuration should the company use?
Set temperature to 0.1 and top_p to 0.9.
Set temperature to 0.7 and top_p to 1.0.
Set temperature to 0.5 and top_p to 0.5.
Set temperature to 0 and top_p to 1.
Temperature 0 makes the model deterministic.
An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?
Amazon CloudFront
Amazon DynamoDB
Amazon S3
Amazon ElastiCache
ElastiCache provides low-latency caching for frequently used data.
A company wants to use a foundation model to automatically moderate user-generated content. The model must filter out inappropriate content with high accuracy. Which Amazon service is best suited for this task?
Amazon Translate
Amazon Rekognition
Amazon Polly
Amazon Comprehend
Comprehend offers content moderation features.
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Practice this domain14% of exam · 6 sample questions below
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?
AWS Secrets Manager
AWS CloudHSM
AWS Key Management Service (KMS)
KMS provides centralized control over encryption keys used to encrypt data at rest in SageMaker.
AWS Certificate Manager
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?
Use AWS Lake Formation to grant cross-account access to the bucket
Copy the S3 bucket to the same account as Bedrock
Configure the S3 bucket policy to allow access from the Bedrock service role and add a Bedrock resource policy allowing the bucket
This two-way policy approach ensures only the specified Bedrock role can access the bucket, following cross-account security best practices.
Make the S3 bucket public and use a pre-signed URL
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?
Topic restrictions
Topic restrictions allow creating a list of denied topics; the model avoids discussing them.
Content filters
Grounding check
Word filters
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?
AWS CloudTrail
CloudTrail records all AWS API calls, including Bedrock, for auditing and compliance.
Amazon S3 server access logs
Amazon CloudWatch Logs
AWS Config
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?
Amazon Macie
Macie automatically discovers sensitive data in S3 using machine learning.
Amazon Inspector
AWS Shield
Amazon GuardDuty
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?
SageMaker Pipelines
SageMaker Experiments
SageMaker Data Wrangler
SageMaker Model Monitor
Model Monitor can schedule bias detection with Clarify and track drift.
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Practice this domain20% of exam · 6 sample questions below
A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?
Amazon SageMaker Studio Lab
Amazon SageMaker Clarify
Amazon SageMaker Autopilot
Autopilot automates model building for tabular data.
Amazon SageMaker JumpStart
A company needs to store large amounts of unstructured training data (images, videos) in a cost-effective manner while ensuring low-latency retrieval for training jobs running on Amazon SageMaker. Which storage solution should be used?
Amazon EFS
Amazon S3
S3 is the best fit for storing unstructured data with low-latency access via S3 endpoints.
Amazon RDS
Amazon EBS
An organization wants to detect anomalies in real-time streaming data from IoT devices. The data includes sensor readings, and the team plans to use a machine learning model. Which AWS service should be used to build and deploy the model with minimal operational overhead?
Amazon SageMaker
SageMaker offers end-to-end ML capabilities and can deploy real-time endpoints.
AWS Glue
Amazon QuickSight
Amazon Kinesis Data Analytics
During a SageMaker training job, the data scientist observes that the loss is not decreasing after the initial few epochs. The model is a deep neural network with ReLU activations. Which hyperparameter adjustment is most likely to help?
Reduce the learning rate
A lower learning rate can allow the optimizer to find a better minimum.
Increase the number of epochs
Increase the learning rate
Decrease the batch size
Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)
AWS Glue
Glue provides ETL capabilities suitable for preprocessing.
Amazon Athena
Amazon SageMaker Data Wrangler
Data Wrangler is specifically for visual data preparation.
Amazon Redshift
AWS Lambda
Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)
It can only be used for text data.
It provides built-in workflows for image classification and object detection.
Ground Truth supports these tasks.
It supports automated data labeling using active learning.
Active learning reduces manual labeling effort.
It integrates with Amazon SageMaker to use the labeled data for training.
Labeled data can be exported to S3 and used in SageMaker training.
It can only use a public workforce from Amazon Mechanical Turk.
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Practice this domain24% of exam · 6 sample questions below
A company is building a chatbot using Amazon Bedrock and wants to ensure that the model generates responses consistent with its brand voice. Which technique should be used to provide the model with examples of desired responses without fine-tuning the model?
Fine-tune the model on a dataset of brand-compliant conversations.
Use prompt chaining to break down the conversation into multiple steps.
Implement a Retrieval Augmented Generation (RAG) system with brand documents.
Include few-shot examples in the system prompt to demonstrate the desired tone.
In-context learning via few-shot examples guides model behavior without retraining.
A data scientist is using Amazon SageMaker to train a large language model from scratch. Which AWS service is most suitable for managing the training infrastructure, including automatic scaling and spot instance recovery?
AWS Lambda function.
Amazon SageMaker Notebook instance.
Amazon SageMaker Training job.
SageMaker Training manages infrastructure, automatically recovers from spot interruptions, and scales.
Amazon EC2 with a custom setup.
A team is using Amazon Bedrock to generate images from text prompts. The generated images often contain artifacts and do not match the prompt description. Which combination of steps should the team take to improve image quality?
Fine-tune the model using SageMaker Ground Truth and increase the training epochs.
Increase the max token count and use a larger model variant.
Refine the prompt with more descriptive language and adjust the CFG scale and inference steps.
Better prompts and tuning inference parameters directly improve image quality.
Use a different foundation model and increase the image resolution.
A developer is creating a generative AI application using Amazon Bedrock and needs to ensure that responses do not include toxic or harmful content. Which feature should be enabled?
Amazon CloudWatch Logs for prompt logging.
Amazon Virtual Private Cloud (VPC) for network isolation.
Amazon Bedrock Guardrails.
Guardrails enforce content policies, filter toxic content, and block denied topics.
AWS Identity and Access Management (IAM) policies.
A company is using Amazon SageMaker JumpStart to deploy a pre-trained text generation model. After deployment, the model produces slow inference responses. Which action is most likely to improve inference latency?
Quantize the model weights to FP16 or INT8.
Deploy the model on a more powerful instance type with higher GPU memory.
More compute resources reduce inference time per request.
Fine-tune the model on a smaller dataset.
Increase the batch size for inference requests.
An organization is using Amazon Bedrock to power a customer service chatbot. They notice that the chatbot occasionally generates hallucinated information about product specifications. Which strategy should be implemented to reduce hallucinations?
Fine-tune the model on a dataset of product specification conversations.
Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.
RAG provides up-to-date, factual context to the model, reducing hallucinations.
Use more detailed prompts with explicit instructions to avoid speculation.
Increase the temperature parameter to make outputs more conservative.
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Practice this domain14% of exam · 6 sample questions below
A financial services company uses Amazon Rekognition to verify customer identities. To ensure responsible AI practices, which measure should the company prioritize?
Use only black-box models to protect intellectual property
Increase model complexity to improve accuracy
Minimize the amount of training data collected
Regularly audit the model for demographic bias
Bias audits are essential for fairness.
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?
Increase the prediction threshold for the affected group
Use Amazon SageMaker Clarify to detect bias in predictions
Clarify provides bias metrics to inform next steps.
Retrain the model with more data from the affected group
Immediately retire the model to prevent harm
A company uses an AI system to automate loan approvals. The model uses demographic features and achieves high accuracy, but the company wants to ensure compliance with responsible AI guidelines. Which practice best balances performance and fairness?
Use demographic features but with minimal monitoring
Use a complex black-box model and rely on post-hoc explanations
Remove sensitive attributes and monitor for proxy bias
Removing attributes reduces direct bias, monitoring detects proxies.
Optimize the model solely for accuracy on historical data
A retail company uses a recommendation system that occasionally suggests inappropriate products to minors. Which responsible AI practice should be applied?
Implement human review of flagged recommendations
Human-in-the-loop ensures responsible oversight.
Rely solely on user feedback to improve
Disable the recommendation system entirely
Increase the volume of training data
A company uses Amazon Comprehend to analyze customer sentiment. They discover the model performs poorly on text with slang from underrepresented groups. What is the most responsible action?
Restrict model use to only standard English
Remove slang from input before inference
Adjust the confidence threshold only for those groups
Collect more representative training data including slang
Representative data reduces bias.
A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?
Send all flagged transactions to customers for confirmation
Focus only on precision to minimize false positives
Tune the model to achieve an acceptable balance between recall and precision
Balancing metrics is a responsible approach.
Increase the detection threshold to reduce false positives
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Practice this domainThe AIF-C01 exam has 50 questions and must be completed in 90 minutes. The passing score is 700/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 5 domains: Applications of Foundation Models, Security, Compliance, and Governance for AI Solutions, Fundamentals of AI and ML, Fundamentals of Generative AI, Guidelines for Responsible AI. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Amazon Web Services AIF-C01 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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