Amazon MLA-C01 Dumps Questions Answers

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Amazon MLA-C01 Frequently Asked Questions

Amazon MLA-C01 Sample Questions

Question # 1

A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data. Which technique for feature engineering should the ML engineer use for the model? 

A. Apply label encoding to the color categories. Automatically assign each color a unique integer. 
B. Implement padding to ensure that all color feature vectors have the same length. 
C. Perform dimensionality reduction on the color categories. 
D. One-hot encode the color categories to transform the color scheme feature into a binary matrix. 

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Question # 2

An ML engineer is using AWS CodeDeploy to deploy new container versions for inference on Amazon ECS. The deployment must shift 10% of traffic initially, and the remaining 90% must shift within 10–15 minutes. Which deployment configuration meets these requirements? 

A. CodeDeployDefault.LambdaLinear10PercentEvery10Minutes
 B. CodeDeployDefault.ECSAllAtOnce 
C. CodeDeployDefault.ECSCanary10Percent15Minutes
 D. CodeDeployDefault.LambdaCanary10Percent15Minutes 

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Question # 3

A company runs an ML model on Amazon SageMaker AI. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker AI from collecting metadata from the training jobs? 

A. Opt out of metadata tracking for any training job that is submitted. 
B. Ensure that training jobs are running in a private subnet in a custom VPC. 
C. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key. 
D. Reconfigure the training jobs to use only AWS Nitro instances. 

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Question # 4

A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories. Which solution will meet these requirements? 

A. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account. 
B. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross-account replication between the initial ECR repositories and the central catalog. 
C. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts. 
D. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure crossaccount access to the Data Catalog. 

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Question # 5

A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes. Which algorithm and hyperparameter should the company use to meet this requirement? 

A. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity. 
B. Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters. 
C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations. 
D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater than 100. 

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Question # 6

A company uses the Amazon SageMaker AI Object2Vec algorithm to train an ML model. The model performs well on training data but underperforms after deployment. The company wants to avoid overfitting the model and maintain the model's ability to generalize. Which solution will meet these requirements? 

A. Decrease the early_stopping_patience hyperparameter. 
B. Increase the mini_batch_size hyperparameter. 
C. Decrease the dropout rate.
 D. Increase the number of epochs. 

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Question # 7

A company needs an AWS solution that will automatically create versions of ML models as the models are created. Which solution will meet this requirement?

A. Amazon Elastic Container Registry (Amazon ECR) 
B. Model packages from Amazon SageMaker Marketplace 
C. Amazon SageMaker ML Lineage Tracking 
D. Amazon SageMaker Model Registry 

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Question # 8

A company launches a feature that predicts home prices. An ML engineer trained a regression model using the SageMaker AI XGBoost algorithm. The model performs well on training data but underperforms on real-world validation data. Which solution will improve the validation score with the LEAST implementation effort?

A. Create a larger training dataset with more real-world data and retrain. 
B. Increase the num_round hyperparameter. 
C. Change the eval_metric from RMSE to Error. 
D. Increase the lambda hyperparameter. 

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Question # 9

A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account. An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses. Which solution will meet these requirements?

A. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0. 
B. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0. 
C. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3. 
D. Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift. 

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Question # 10

An ML engineer wants to re-train an XGBoost model at the end of each month. A data team prepares the training data. The training dataset is a few hundred megabytes in size. When the data is ready, the data team stores the data as a new file in an Amazon S3 bucket. The ML engineer needs a solution to automate this pipeline. The solution must register the new model version in Amazon SageMaker Model Registry within 24 hours. Which solution will meet these requirements?

A. Create an AWS Lambda function that runs one time each week to poll the S3 bucket for new files. Invoke the Lambda function asynchronously. Configure the Lambda function to start the pipeline if the function detects new data. 
B. Create an Amazon CloudWatch rule that runs on a schedule to start the pipeline every 30 days.
 C. Create an S3 Lifecycle rule to start the pipeline every time a new object is uploaded to the S3 bucket. 
D. Create an Amazon EventBridge rule to start an AWS Step Functions TrainingStep every time a new object is uploaded to the S3 bucket. 

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Question # 11

An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents. The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras. Which solution will improve the model's accuracy in the LEAST amount of time? 

A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset. 
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option. 
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option. 
D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size. 

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Question # 12

Case study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data. Which solution will meet this requirement with the LEAST operational effort? 

A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly. 
B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset. 
C. Use AWS Glue DataBrew built-in features to oversample the minority class. 
D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class. 

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Question # 13

A company is developing an ML model by using Amazon SageMaker AI. The company must monitor bias in the model and display the results on a dashboard. An ML engineer creates a bias monitoring job. How should the ML engineer capture bias metrics to display on the dashboard?

A. Capture AWS CloudTrail metrics from SageMaker Clarify. 
B. Capture Amazon CloudWatch metrics from SageMaker Clarify. 
C. Capture SageMaker Model Monitor metrics from Amazon EventBridge. 
D. Capture SageMaker Model Monitor metrics from Amazon SNS. 

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Question # 14

An ML engineer is training an XGBoost regression model in Amazon SageMaker AI. The ML engineer conducts several rounds of hyperparameter tuning with random grid search. After these rounds of tuning, the error rate on the test hold-out dataset is much larger than the error rate on the training dataset. The ML engineer needs to make changes before running the hyperparameter grid search again. Which changes will improve the model's performance? (Select TWO.) 

A. Increase the model complexity by increasing the number of features in the dataset. 
B. Decrease the model complexity by reducing the number of features in the dataset. 
C. Decrease the model complexity by reducing the number of samples in the dataset. 
D. Increase the value of the L2 regularization parameter. 
E. Decrease the value of the L2 regularization parameter. 

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Question # 15

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months. Which EC2 instance purchasing option will meet these requirements MOST costeffectively?

A. Spot Instances 
B. Reserved Instances 
C. On-Demand Instances 
D. Dedicated Instances 

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Question # 16

A retail company is analyzing customer purchase data to develop personalized product recommendations. The company wants to use Amazon SageMaker Clarify to assess fairness metrics across different customer groups to avoid potential bias in the recommendation system. The recommendation system needs to identify if certain customer segments are underrepresented in the training data. The company needs to choose a pre-training bias metric in SageMaker Clarify. Which metric meets these requirements?

A. Prediction distribution skew 
B. Feature attribution bias 
C. Class imbalance ratio 
D. Model performance gap 

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Question # 17

A recommendation model uses ML and calls an Amazon SageMaker AI endpoint to get recommendations. An ML engineer must ensure that the model stays available during an expected increase in user traffic. Which solution will meet these requirements?

A. Configure auto scaling on the SageMaker AI endpoint. 
B. Create a new SageMaker AI endpoint. Deploy the model to the new endpoint. 
C. Use SageMaker Neo to optimize the model for inference. 
D. Attach an Auto Scaling group to the SageMaker AI endpoint. 

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Question # 18

A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance. The company needs to centralize management of the team's permissions. Which solution will meet this requirement?

A. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses. 
B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses. 
C. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user. 
D. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses. 

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Question # 19

An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time. Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent. Which solution will meet this requirement? 

A. Compilation 
B. Speculative decoding 
C. Quantization 
D. Fast model loading 

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Question # 20

A streaming media company uses a churn risk model to assess the churn risk of its premium tier customers. Each month, the company runs an aggregation job on individual customers’ streaming data and uploads the user engagement features to an Amazon S3 bucket. The company manually re-trains the churn risk model with the user engagement data. The current process requires manual intervention and is time-consuming. The company needs a solution that automatically re-trains the churn prediction model with the most recent data. Which solution will meet these requirements with the SHORTEST delay?

A. Set up an Amazon EventBridge rule to run an Amazon Elastic Container Service (Amazon ECS) task hourly for model re-training. Configure the ECS task to use the most recent data from the S3 bucket. 
B. Configure the S3 bucket to invoke an AWS Lambda function that re-trains the model. 
C. Create a pipeline in Amazon SageMaker Pipelines for re-training. Configure an Amazon EventBridge rule to monitor S3 PutObject creation events and invoke the pipeline. 
D. Create a pipeline in Amazon SageMaker Pipelines for re-training. Configure a pipeline schedule to re-train the model. 

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Question # 21

An ML engineer is using Amazon SageMaker AI to train an ML model. The ML engineer needs to use SageMaker AI automatic model tuning (AMT) features to tune the model hyperparameters over a large parameter space. The model has 20 categorical hyperparameters and 7 continuous hyperparameters that can be tuned. The ML engineer needs to run the tuning job a maximum of 1,000 times. The ML engineer must ensure that each parameter trial is built based on the performance of the previous trial. Which solution will meet these requirements?

A. Define the search space as categorical parameters of 1,000 possible combinations. Use grid search. 
B. Define the search space as continuous parameters. Use random search. Set the maximum number of tuning jobs to 1,000. 
C. Define the search space as categorical parameters and continuous parameters. Use Bayesian optimization. Set the maximum number of training jobs to 1,000. 
D. Define the search space as categorical parameters and continuous parameters. Use grid search. Set the maximum number of tuning jobs to 1,000. 

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Question # 22

An ML engineer needs to run intensive model training jobs each month that can take 48–72 hours. The jobs can be interrupted and resumed. The engineer has a fixed budget and needs the most cost-effective compute option. Which solution will meet these requirements?

A. Purchase Reserved Instances with partial upfront payment. 
B. Purchase On-Demand Instances. 
C. Purchase SageMaker AI Savings Plans. 
D. Purchase Spot Instances that use automated checkpoints. 

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Question # 23

A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality and model performance. Which solution will meet these requirements?

A. Schedule a monitoring job in Amazon SageMaker Model Monitor. Generate the monitoring results for the model and data. 
B. Create an Amazon CloudWatch dashboard that includes the metrics for processing steps in the nightly batch inference job. Compare the baseline resource metrics. Share the dashboard link. 
C. Use AWS Glue DataBrew to create a custom recipe job that uses the Numerical Statistics data quality check for the model file. Generate the results. 
D. Create a SageMaker AI pipeline that includes a QualityCheck step to run monitoring jobs. Generate the monitoring results for the model and the data. 

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Question # 24

An ML engineer at a credit card company built and deployed an ML model by using Amazon SageMaker AI. The model was trained on transaction data that contained very few fraudulent transactions. After deployment, the model is underperforming. What should the ML engineer do to improve the model’s performance? What should the ML engineer do to improve the model’s performance?

 A. Retrain the model with a different SageMaker built-in algorithm. 
B. Use random undersampling to reduce the majority class and retrain the model. 
C. Use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic minority samples and retrain the model.
 D. Use random oversampling to duplicate minority samples and retrain the model. 

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Question # 25

An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata. Which solution will meet these requirements with the LEAST manual effort? 

A. Use AWS Glue to run the ETL job. Use the job to discover the schema and to store the associated metadata in the AWS Glue Data Catalog. 
B. Create an Amazon SageMaker Data Wrangler flow to run the ETL job. Use the job to discover the schema and to store the associated metadata in an S3 bucket. 
C. Create an ETL pipeline by using Amazon Athena integrated with AWS Step Functions. Use the pipeline to run the ETL job to discover the schema and to store the associated metadata in an S3 bucket. 
D. Launch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job. Use the job to discover the schema and to store the associated metadata in Amazon Redshift. 

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Question # 26

A company wants to improve the sustainability of its ML operations. Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected. 
B. Use Amazon SageMaker Ground Truth for data labeling. 
C. Deploy models by using AWS Lambda functions.
 D. Use AWS Trainium instances for training.
 E. Use PyTorch or TensorFlow with the distributed training option. 

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Question # 27

A company is building a near real-time data analytics application to detect anomalies and failures for industrial equipment. The company has thousands of IoT sensors that send data every 60 seconds. When new versions of the application are released, the company wants to ensure that application code bugs do not prevent the application from running. Which solution will meet these requirements?

A. Use Amazon Managed Service for Apache Flink with the system rollback capability enabled to build the data analytics application. 
B. Use Amazon Managed Service for Apache Flink with manual rollback when an error occurs to build the data analytics application. 
C. Use Amazon Data Firehose to deliver real-time streaming data programmatically for the data analytics application. Pause the stream when a new version of the application is released and resume the stream after the application is deployed. 
D. Use Amazon Data Firehose to deliver data to Amazon EC2 instances across two Availability Zones for the data analytics application. 

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Question # 28

An ML engineering team has a data processing pipeline that ingests sensor data from IoT devices into an Amazon S3 bucket. The pipeline then processes the data by using AWS Glue extract, transform, and load (ETL) jobs for ML modeling. The team noticed throttling errors in the ETL jobs. The data ingestion process has also been slower than normal. What is the cause of the problem?

A. The AWS Glue service quotas have been reached. 
B. The network bandwidth between the IoT devices and the AWS Region is insufficient. 
C. The AWS Glue ETL jobs are not optimized for parallel processing. 
D. The AWS Glue execution role is missing Amazon S3 permissions. 

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Question # 29

A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns. Which solution will meet these requirements?

A. Use the principal component analysis (PCA) algorithm in Amazon SageMaker AI. 
B. Use the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm in Amazon SageMaker AI. 
C. Use the k-means algorithm in Amazon SageMaker AI. 
D. Use the Random Cut Forest (RCF) algorithm in Amazon SageMaker AI. 

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