Free AWS Certified Machine Learning – Specialty MLS-C01 Exam Practice Test

A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.Which solution will meet these requirements with the LEAST operational overhead?

• Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
• Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
• Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
• Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.

A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.Which techniques should the company use for feature selection? (Choose three.)

• Data scaling with standardization and normalization
• Correlation plot with heat maps
• Data binning
• Univariate selection
• Feature importance with a tree-based classifier
• Data augmentation

A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.Which solution will meet these requirements with the LEAST development effort?

• Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
• Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.
• Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
• Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

• Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
• AWS Glue with a custom ETL script to transform the data.
• An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
• Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3.The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)

• Use Amazon Athena to scan the data and identify the schema.
• Use AWS Glue crawlers to scan the data and identify the schema.
• Use Amazon Redshift to store procedures to perform data transformations
• Use AWS Glue workflows and AWS Glue jobs to perform data transformations.
• Use Amazon Redshift ML to train a model to detect fraud.
• Use Amazon Fraud Detector to train a model to detect fraud.

A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.How should the Data Science team configure the notebook instance placement to meet these requirements?

• Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
• Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
• Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
• Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulentHow should the Specialist frame this business problem'?

• Streaming classification
• Binary classification
• Multi-category classification
• Regression classification

A Machine Learning Specialist works for a credit card processing company and needs to predict whichtransactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns theprobability that a given transaction may fraudulent.How should the Specialist frame this business problem?

• Streaming classification
• Binary classification
• Multi-category classification
• Regression classification

A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.Which completion criteria will meet this requirement?

• MaxRuntimelnSeconds
• TargetObjectiveMetricValue
• CompleteOnConvergence
• MaxNumberOfTrainingJobsNotlmproving

A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that 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 is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

• Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
• Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
• Create a line chart with the weekly mean absolute error (MAE) of the model.
• Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.