MLS-C01 AWS Certified Machine Learning Specialty Test Paper

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Sample Questions: Q) A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive. Based on the model evaluation results, why is this a viable model for production?A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives. B. The precision of the model is 86%, which is less than the accuracy of the model. C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives. D. The precision of the model is 86%, which is greater than the accuracy of the model. Q) A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users. What should the Specialist do to meet this objective?A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMRB. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMRD. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMRQ) A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users. What should the Specialist do to meet this objective?A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMRB. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMRD. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMRQ) A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available. Which model is MOST likely to provide the best results in Amazon Sage Maker?A. Use the Amazon Sage Maker k-Nearest-Neighbors (k NN) algorithm on the single time series consisting of the full year of data with a predictor type of regressor. B. Use Amazon Sage Maker Random Cut Forest (RCF) on the single time series consisting of the full year of data. C. Use the Amazon Sage Maker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor type of regressor. D. Use the Amazon Sage Maker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor type of classifier. Q) A Data Engineer needs to build a model using a dataset containing customer credit card information. How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon Sage Maker instance in a VPC. Use the Sage Maker Deep AR algorithm to randomize the credit card numbers. B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers. C. Use an Amazon Sage Maker launch configuration to encrypt the data once it is copied to the Sage Maker instance in a VPC. Use the Sage Maker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers. D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon Sage Maker, and redact the credit card numbers from the customer data with AWS Glue. Q) A Machine Learning Specialist is using an Amazon Sage Maker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon Sage Maker notebook instance’s Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon Sage Maker notebook instance’s EBS volume or Amazon EC2 instance within the VPC. Why is the ML Specialist not seeing the instance visible in the VPC?A. Amazon Sage Maker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs. B. Amazon Sage Maker notebook instances are based on the Amazon ECS service within customer accounts. C. Amazon Sage Maker notebook instances are based on EC2 instances running within AWS service accounts. D. Amazon Sage Maker notebook instances are based on AWS ECS instances running within AWS service accounts.