Free AIP-C01 Practice Test Questions and Answers (2026) | Cert Empire

A. Route telemetry data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core, configure a rule in IoT Core to direct the data to an Amazon Data Firehose stream that delivers data to an S3.

B. Use AWS IoT Greengrass on each device to preprocess telemetry data locally, then batch upload the data to S3 using AWS SDK calls from the edge.

C. Set up a serverless application with Amazon API Gateway to collect telemetry data from the devices, then use AWS Lambda to process and deliver the data to S3.

D. Stream the telemetry data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core, forward it to an Amazon Kinesis Data Stream, and then configure an AWS Lambda function to process the data and send it to S3.

A. Use grid search to evaluate all possible hyperparameter combinations without early stopping exhaustively.

B. Utilize the Hyperband strategy in SageMaker AI to allocate resources efficiently and early stop weak trials.

C. Implement Bayesian optimization to refine the hyperparameter search space iteratively.

D. Utilize random search to sample hyperparameter combinations uniformly across the search space.

A. The issue is due to a high learning rate, which causes the optimization process to skip over the optimal weights during training.

B. The issue is due to Class Imbalance (CI) in the training dataset, where the minority class has too few samples, causing the model to learn biased decision boundaries.

C. The issue is due to insufficient text extraction, where Textract failed to extract all key fields, leading to missing features for the model.

D. The issue is due to overfitting, where the model has memorized training examples and performs poorly on unseen data, regardless of label distribution.

A. Use SageMaker Studio to preprocess the data and apply SMOTE, then use SageMaker Reinforcement Learning to build a fraud detection model and check for bias with SageMaker Clarify.

B. Use SageMaker Studio for data processing and model development, integrating the synthetic minority oversampling technique (SMOTE) into the workflow. Once the model is trained, use Amazon Augmented AI (Amazon A2I) for bias detection before deployment.

C. Use SageMaker Studio for data processing and model development, integrating the synthetic minority oversampling technique (SMOTE) into the workflow. Once the model is trained, use Amazon Augmented AI (Amazon A2I) for bias detection before deployment.

D. Use SageMaker Studio to preprocess the data and apply the synthetic minority oversampling technique (SMOTE) to balance the dataset. Build the model using SageMaker Pipelines and use SageMaker Clarify for bias detection before deployment.

A. Start a warm start hyperparameter tuning job using the TRANSFER_LEARNING warm start type to import the previously saved tuning job results. Enable AMT Early Stopping to automatically terminate exploration as soon as validation loss stops improving when training with the new quarterly dataset.

B. Execute a hyperparameter tuning job with a newly expanded search space using the same algorithm container. Rely on Bayesian Optimization inside SageMaker AI AMT to naturally rediscover higher-performing parameter combinations using the new quarterly dataset, which will naturally guide convergence efficiency without importing prior tuning job results.

C. Run a new hyperparameter tuning job using SageMaker AMT Hyperband strategy, allowing the system to aggressively eliminate poorly performing training jobs early while exploring the new quarterly dataset with wider search ranges. Depend on Hyperband efficiency itself rather than importing prior warm start knowledge from the previously tuned model.

D. Configure a warm start hyperparameter tuning job with the IDENTICAL_DATA_AND_ALGORITHM warm start mode and enable AMT Early Stopping to automatically terminate exploration as soon as validation loss stops improving.

A. Utilize Amazon Transcribe for audio and video-to-text conversion, Amazon Translate for translating the content into English, and Amazon Bedrock with the Jamba model for summarizing the text.

B. Train a custom model in Amazon SageMaker AI to process the data into English, then deploy an LLM in SageMaker AI for summarizing the content.

C. Use AWS Glue to clean and prepare the data, then use Amazon Translate to translate the data into English, and summarize the content using Amazon Lex to create a conversational summary.

D. Leverage Amazon Translate to translate the text into English, apply a pre- trained model in Amazon SageMaker AI for analysis, and summarize the content using the Claude Anthropic model in Amazon Bedrock.

A. Increase the instance size of the EC2 On-Demand Instance to a larger GPU type for higher throughput.

B. Use Rekognition Custom Labels to optimize image access and retrain the model.

C. Change the SageMaker training jobʼs data input configuration to FastFile mode to stream data directly from S3 without other changes.

D. Set the SageMaker training job to Pipe mode instead of File mode to enhance training throughput.

A. Leverage SageMaker Autopilot to automatically train a classification model for forecasting customer churn. Then, utilize insights from SageMaker Clarify to determine which features most significantly influence the predictions.

B. Use SageMaker Data Wrangler to automatically train a churn prediction model and rely on its quick model visualization feature to generate accurate importance scores for deployment decisions.

C. Use the k-means algorithm in SageMaker AI to cluster customers based on purchasing patterns. After clustering, use the resulting clusters to predict churn based on customer behavior.

D. Use SageMaker Ground Truth to label customer churn data, then build a custom TensorFlow model to predict churn and analyze feature weights post- training.

A. Use ModelExplainabilityMonitor class with a SHAP-based baseline to detect feature attribution drift in production. Regularly compare how the model assigns importance to input features against the baseline and configure Amazon CloudWatch to alert stakeholders when attribution values drift beyond acceptable thresholds.

B. Implement a baseline for model quality using the ModelQualityMonitor class. The baseline will evaluate key performance metrics such as accuracy and recall, with periodic checks to identify any significant shifts in model performance. Set up an Amazon CloudWatch if the modelʼs quality metrics diverge from the baseline.

C. Enable SageMaker DataCapture to log inference inputs and outputs. Build a custom pipeline to analyze feature distributions and model responses over time. Use Amazon CloudWatch to alert when significant shifts in input patterns or predictions are detected.

D. Deploy SageMaker Clarify to perform bias and explainability analysis on the training dataset. Use Amazon CloudWatch to alert if Clarify reports significant changes in feature attribution or fairness metrics.

A. Set up VPC Traffic Mirroring to capture traffic to and from the notebook instances and identify unauthorized access attempts, enabling enhanced monitoring.

B. Apply VPC Endpoint Policies to control which IAM users or services can access SageMaker AI through the VPC interface endpoint, providing more granular access control for interactions with SageMaker AI.

C. Configure an IAM policy that allows sagemaker:CreatePresignedNotebookInstanceUrl and sagemaker:DescribeNotebookInstance actions exclusively from VPC interface endpoints. Ensure this policy is applied to the appropriate IAM users, groups, and roles.

D. Update the security group for the notebook instances to restrict incoming traffic to only the CIDR blocks associated with the VPC. Apply this security group across all interfaces linked to the SageMaker notebook instances.