Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 111:

  A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.

  After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.

  Which solutions will meet this requirement? (Select TWO.)

  A. Increase the number of passes on the existing training data. Perform more hyperparameter tuning.
  B. Increase the amount of regularization. Use fewer feature combinations.
  C. Add new domain-specific features. Use more complex models.
  D. Use fewer feature combinations. Decrease the number of numeric attribute bins.
  E. Decrease the amount of training data examples. Reduce the number of passes on the existing training data.
  A. Increase the number of passes on the existing training data. Perform more hyperparameter tuning.
  C. Add new domain-specific features. Use more complex models.

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  Explanation

  For a model with low accuracy on both training and testing datasets, the following two strategies are effective:

  Increase the number of passes and perform hyperparameter tuning: This approach allows the model to better learn from the existing data and improve performance through optimized hyperparameters. Add domain-specific features and use

  more complex models: Adding relevant features that capture additional information from domain knowledge and using more complex model architectures can help the model capture patterns better, potentially improving accuracy.

  Options B, D, and E would either reduce feature complexity or training data volume, which is less likely to improve performance when accuracy is low on both training and testing sets.

• Question 112:

  A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future.

  Which solution will meet these requirements with the LEAST amount of operational overhead?

  A. Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
  B. Use Amazon SageMaker Model Monitor to create a model quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.
  C. Use Amazon SageMaker Debugger to create rules to capture feature values Set up an Amazon CloudWatch alarm for the rules.
  D. Use Amazon CloudWatch to monitor Amazon SageMaker endpoints. Analyze logs in Amazon CloudWatch Logs to check for data drift.
  A. Use Amazon SageMaker Model Monitor to create a data quality baseline. Confirm that the emit_metrics option is set to Enabled in the baseline constraints file. Set up an Amazon CloudWatch alarm for the metric.

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  Explanation

  "If the statistical nature of the data that your model receives while in production drifts away from the nature of the baseline data it was trained on, the model begins to lose accuracy in its predictions. Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens." https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-quality.html

• Question 113:

  A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data.

  Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)

  A. Use SageMaker Clarify to automatically detect data bias
  B. Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.
  C. Use SageMaker Model Monitor to generate a bias drift report.
  D. Configure SageMaker Data Wrangler to generate a bias report.
  E. Use SageMaker Experiments to perform a data check
  A. Use SageMaker Clarify to automatically detect data bias
  D. Configure SageMaker Data Wrangler to generate a bias report.

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  Explanation

• Question 114:

  A company is building a predictive maintenance system using real-time data from devices on remote sites. There is no AWS Direct Connect connection or VPN connection between the sites and the company's VPC. The data needs to be ingested in real time from the devices into Amazon S3. Transformation is needed to convert the raw data into clean .csv data to be fed into the machine learning (ML) model. The transformation needs to happen during the ingestion process. When transformation fails, the records need to be stored in a specific location in Amazon S3 for human review. The raw data before transformation also needs to be stored in Amazon S3. How should an ML specialist architect the solution to meet these requirements with the LEAST effort?

  A. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an AWS Lambda function for data transformation. Enable source record backup on Firehose.
  B. Use Amazon Managed Streaming for Apache Kafka. Set up workers in Amazon Elastic Container Service (Amazon ECS) to move data from Kafka brokers to Amazon S3 while transforming it. Configure workers to store raw and unsuccessfully transformed data in different S3 buckets.
  C. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an Apache Spark job in AWS Glue for data transformation. Enable source record backup and configure the error prefix.
  D. Use Amazon Kinesis Data Streams in front of Amazon Data Firehose. Use Kinesis Data Streams with AWS Lambda to store raw data in Amazon S3. Configure Firehose to invoke a Lambda function for data transformation with Amazon S3 as the destination.
  A. Use Amazon Data Firehose with Amazon S3 as the destination. Configure Firehose to invoke an AWS Lambda function for data transformation. Enable source record backup on Firehose.

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  Explanation

• Question 115:

  A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's

  data currently resides on premises and is 40 in size.

  The company wants a solution that can transfer and automatically update data between the on-premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation.

  Which solution meets these requirements?

  A. Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified.
  B. Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3.
  C. Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.
  D. Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.
  C. Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS.

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  Explanation

  Configure DataSync to make an initial copy of your entire dataset, and schedule subsequent incremental transfers of changing data until the final cut-over from on-premises to AWS.

  https://aws.amazon.com/datasync/faqs/

• Question 116:

  A company's machine learning (ML) team needs to build a system that can detect whether people in a collection of images are wearing the company's logo. The company has a set of labeled training data. Which algorithm should the ML team use to meet this requirement?

  A. Principal component analysis (PCA)
  B. Recurrent neural network (RNN)
  C. -nearest neighbors (k-NN)
  D. Convolutional neural network (CNN)
  D. Convolutional neural network (CNN)

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  Explanation

• Question 117:

  A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor.

  Which machine learning approach fulfills the company's long-term needs?

  A. Convert the images to grayscale and retrain the model
  B. Reduce the number of distinct items from 10 to 2, build the model, and iterate
  C. Attach different colored labels to each item, take the images again, and build the model
  D. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
  A. Convert the images to grayscale and retrain the model

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  Explanation

• Question 118:

  A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.

  What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)

  A. AWS Secrets Manager
  B. AWS CodeStar
  C. Amazon ECR
  D. Amazon ECS
  E. Amazon S3
  C. Amazon ECR
  E. Amazon S3

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  Explanation

  https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-create-model-package.html

• Question 119:

  A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.

  How can the ML team solve this issue?

  A. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.
  B. Replace the current endpoint with a multi-model endpoint using SageMaker.
  C. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
  D. Increase the cooldown period for the scale-out activity.
  D. Increase the cooldown period for the scale-out activity.

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  Explanation

  https://aws.amazon.com/blogs/machine-learning/configuring-autoscaling-inference-endpoints-in-amazon-sagemaker/

• Question 120:

  A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches

  What actions would allow the Specialist to get relevant numerical representations?

  A. Reduce image resolution and use reduced resolution pixel values as features
  B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
  C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
  D. Average colors by channel to obtain three-dimensional representations of images.
  A. Reduce image resolution and use reduced resolution pixel values as features

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  Explanation