A company is ingesting IoT sensor data from millions of devices into Amazon Kinesis Data Streams. The data science team needs to transform the stream in real-time to: 1) filter out readings from faulty sensors, 2) convert temperature from Celsius to Fahrenheit, and 3) enrich the data with device metadata from a DynamoDB table. The transformed data must be available for real-time dashboards and also stored in S3 for batch analytics. The team has a preference for using SQL for the transformations. Which solution should they use?

Use a Spark Structured Streaming job on Amazon EMR to process the stream, and write the output to S3.

Use AWS Glue streaming ETL to process the stream with PySpark, and write to S3.

Use AWS Lambda functions to process each record, filter, convert, and enrich, then write to Kinesis Data Firehose for S3 storage.

Use Kinesis Data Analytics for SQL to run SQL queries on the stream, perform the transformations, and write to both a Kinesis Data Stream for dashboards and Firehose for S3.

AWSCertified Machine Learning Engineer - Associate

Domain 1Objective 2

Task 1.2: Transform Data and Perform Feature Engineering MLA-C01 Practice Questions (Page 1)

Part of the Content Domain 1: Data Preparation for Machine Learning (ML) domain, which accounts for 28% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~14–24 in this domain), expect 5–8 from this objective — we provide 25 practice questions to prepare you well beyond it. (estimate)

25questions here

5free pages

9concepts

28%of the exam

Questions 1–5

1foundation · easy
In Amazon SageMaker Feature Store, what is the term for a collection of related features that are stored together and can be shared across multiple ML models?
Select an answer first
2foundation · easy
A feature has values that are heavily right-skewed, and a machine learning engineer wants to reduce the impact of extreme values. Which feature engineering technique is most appropriate for this purpose?
Select an answer first
3expert · hard
A company is ingesting IoT sensor data from millions of devices into Amazon Kinesis Data Streams. The data science team needs to transform the stream in real-time to: 1) filter out readings from faulty sensors, 2) convert temperature from Celsius to Fahrenheit, and 3) enrich the data with device metadata from a DynamoDB table. The transformed data must be available for real-time dashboards and also stored in S3 for batch analytics. The team has a preference for using SQL for the transformations. Which solution should they use?
Select an answer first
4foundation · easy
A streaming application needs to transform each incoming event in real time as it arrives. Which AWS service is commonly used to run lightweight transformation code on individual streaming events?
Select an answer first
5foundation · easy
A feature engineering technique converts a continuous numeric feature into discrete categories, such as grouping ages into 'child', 'adult', and 'senior'. What is this technique called?
Select an answer first

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Task 1.2: Transform Data and Perform Feature Engineering MLA-C01 Practice Questions (Page 1)

Questions 1–5

In Amazon SageMaker Feature Store, what is the term for a collection of related features that are stored together and can be shared across multiple ML models?

A feature has values that are heavily right-skewed, and a machine learning engineer wants to reduce the impact of extreme values. Which feature engineering technique is most appropriate for this purpose?

A streaming application needs to transform each incoming event in real time as it arrives. Which AWS service is commonly used to run lightweight transformation code on individual streaming events?

A feature engineering technique converts a continuous numeric feature into discrete categories, such as grouping ages into 'child', 'adult', and 'senior'. What is this technique called?

Keep studying — Content Domain 1: Data Preparation for Machine Learning (ML)