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AWS Certified Specialty MLS-C01 Exam Dumps

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Total 322 questions

AWS Certified Machine Learning - Specialty Questions and Answers

Question 5

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily

Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

Options:

A.

Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation

B.

Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3

C.

Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

D.

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.

Question 6

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.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

Options:

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.

Question 7

A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

Options:

A.

Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

B.

Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.

C.

Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.

D.

Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

Question 8

A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.

Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.

Which solution will meet these requirements with the LEAST implementation effort?

Options:

A.

Use Amazon EMR Serveriess with PySpark.

B.

Use AWS Glue DataBrew.

C.

Use Amazon SageMaker Studio Data Wrangler.

D.

Use Amazon SageMaker Studio Notebook with Pandas.

Page: 2 / 23
Total 322 questions