Amazon AWS Certified Machine Learning - Specialty
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#191 (Accuracy: 100% / 3 votes)
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet this requirements?
  • A. Call the CreateNotebookInstanceLifecycleConfig API operation
  • B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
  • C. Stop and then restart the SageMaker notebook instance
  • D. Call the UpdateNotebookInstanceLifecycleConfig API operation
#192 (Accuracy: 100% / 5 votes)
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?
  • A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
  • B. Drop the features with low correlation scores by using a Jupyter notebook.
  • C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
  • D. Concatenate the features with high correlation scores by using a Jupyter notebook.
#193 (Accuracy: 100% / 6 votes)
A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?
  • A. Master node
  • B. Any of the core nodes
  • C. Any of the task nodes
  • D. Both core and task nodes
#194 (Accuracy: 100% / 3 votes)
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
  • A. Execute the built-in FindDuplicates Amazon Athena query.
  • B. Create a FindMatches machine learning transform in AWS Glue.
  • C. Create an AWS Glue crawler to infer duplicate accounts in the source data.
  • D. Search for duplicate accounts in the AWS Glue Data Catalog.
#195 (Accuracy: 100% / 4 votes)
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?
  • A. Use the event tracker in Amazon Personalize to include real-time user interactions.
  • B. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
  • C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
  • D. Add event type and event value fields to the interactions dataset in Amazon Personalize.
#196 (Accuracy: 100% / 4 votes)
A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.
To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high- speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.
Which deployment architecture for the model will address these business requirements?
  • A. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
  • B. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
  • C. Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
  • D. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
#197 (Accuracy: 100% / 4 votes)
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome.
Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?
  • A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
  • C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  • D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
#198 (Accuracy: 100% / 3 votes)
A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.
Which model describes the underlying data in this situation?
  • A. A naive Bayesian model, since the features are all conditionally independent.
  • B. A full Bayesian network, since the features are all conditionally independent.
  • C. A naive Bayesian model, since some of the features are statistically dependent.
  • D. A full Bayesian network, since some of the features are statistically dependent.
#199 (Accuracy: 100% / 4 votes)
A geospatial analysis company processes thousands of new satellite images each day to produce vessel detection data for commercial shipping. The company stores the training data in Amazon S3. The training data incrementally increases in size with new images each day.

The company has configured an Amazon SageMaker training job to use a single ml.p2.xlarge instance with File input mode to train the built-in Object Detection algorithm. The training process was successful last month but is now failing because of a lack of storage. Aside from the addition of training data, nothing has changed in the model training process.

A machine learning (ML) specialist needs to change the training configuration to fix the problem. The solution must optimize performance and must minimize the cost of training.

Which solution will meet these requirements?
  • A. Modify the training configuration to use two ml.p2.xlarge instances.
  • B. Modify the training configuration to use Pipe input mode.
  • C. Modify the training configuration to use a single ml.p3.2xlarge instance.
  • D. Modify the training configuration to use Amazon Elastic File System (Amazon EFS) instead of Amazon S3 to store the input training data.
#200 (Accuracy: 100% / 4 votes)
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.

What should the data scientist do to identify and address training issues with the LEAST development effort?
  • A. Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.
  • B. Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.
  • C. Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
  • D. Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.