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AWS Certified Specialty AWS Certified Machine Learning - Specialty

AWS Certified Machine Learning - Specialty

Last Update Dec 11, 2023
Total Questions : 208

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Questions 4

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant

will default on a credit card payment. The company has collected data from a large number of sources with

thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are

highly correlated, the large number of features slows down the training speed significantly, and that there are

some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of

information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?



Run self-correlation on all features and remove highly correlated features


Normalize all numerical values to be between 0 and 1


Use an autoencoder or principal component analysis (PCA) to replace original features with new features


Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Discussion 0
Questions 5

A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.

Which next step is MOST likely to improve the data ingestion rate into Amazon S3?



Increase the number of S3 prefixes for the delivery stream to write to.


Decrease the retention period for the data stream.


Increase the number of shards for the data stream.


Add more consumers using the Kinesis Client Library (KCL).

Discussion 0
Questions 6

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?



Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.


Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.


Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.


Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Discussion 0
Questions 7

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?



Linear regression is inappropriate. The residuals do not have constant variance.


Linear regression is inappropriate. The underlying data has outliers.


Linear regression is appropriate. The residuals have a zero mean.


Linear regression is appropriate. The residuals have constant variance.

Discussion 0
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