AWS-Certified-Machine-Learning-Specialty Free Practice Test

A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data
Which AWS service should the Data Scientist use?

1. A. Amazon Athena
2. B. Amazon Redshift Spectrum
3. C. AWS Glue
4. D. Amazon QuickSight

Correct Answer: A

A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

1. A. Amazon EMR for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
2. B. Amazon Kinesis Data Analytics for data ingestionAmazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3
3. C. AWS Glue for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
4. D. AWS Data Pipeline for data transferAWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights

Correct Answer: A

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?

1. A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
2. B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
3. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
4. D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Correct Answer: B
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

1. A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
2. B. Use AWS Glue to catalogue the data and Amazon Athena to run queries
3. C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes
4. D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries

Correct Answer: D

A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company’s data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model’s testing accuracy.
Which process will improve the testing accuracy the MOST?

1. A. Use a one-hot encoder for the categorical fields in the datase
2. B. Perform standardization on the financial fields in the datase
3. C. Apply L1 regularization to the data.
4. D. Use tokenization of the categorical fields in the datase
5. E. Perform binning on the financial fields in the datase
6. F. Remove the outliers in the data by using the z-score.
7. G. Use a label encoder for the categorical fields in the datase
8. H. Perform L1 regularization on the financial fields in the datase
9. I. Apply L2 regularization to the data.
10. J. Use a logarithm transformation on the categorical fields in the datase
11. K. Perform binning on the financial fields in the datase
12. L. Use imputation to populate missing values in the dataset.

Correct Answer: B