[RETIRED] Practice Exams | AWS Data Analytics Specialty (Udemy.com)

Preparing for AWS Certified Data Analytics Specialty DAS-C01? This is THE practice exams course to give you the winning edge.

These practice exams have been co-authored by Stephane Maarek and Abhishek Singh who bring their collective experience of passing 18 AWS Certifications to the table.

The tone and tenor of the questions mimic the real exam. Along with the detailed description and “exam alert” provided within the explanations, we have also extensively referenced AWS documentation to get you up to speed on all domain areas being tested for the DAS-C01 exam.

We want you to think of this course as the final pit-stop so that you can cross the winning line with absolute confidence and get AWS Certified! Trust our process, you are in good hands.

All questions have been written from scratch! And more questions are being added over time!

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SAMPLE QUESTION:

A credit card company is looking for a solution that detects anomalies in order to identify fraudulent transactions. The company utilizes Amazon Kinesis to transfer JSON-formatted transaction records from its on-premises database to Amazon S3. The existing dataset comprises 100-column wide records for each transaction. To identify fraudulent transactions, the solution needs to analyze just ten of these columns.

As an AWS Certified Data Analytics Specialist, which of the following would you suggest as the lowest-cost solution that needs the least development work and offers out-of-the-box anomaly detection functionality?

1. Leverage Kinesis Data Analytics to detect anomalies on a data stream from Kinesis Streams by running SQL queries which compute an anomaly score for all transactions and then store all fraudulent transactions in Amazon S3. Use Amazon QuickSight to visualize the results from Amazon S3

2. Transform the data from JSON format to Apache Parquet format using an AWS Glue job. Configure AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Leverage Amazon SageMaker to build an anomaly detection model that can detect fraudulent transactions by ingesting data directly from Amazon S3

3. Leverage Kinesis Data Firehose to detect anomalies on a data stream from Kinesis Streams via a Lambda function which computes an anomaly score for all transactions and stores all fraudulent transactions in Amazon RDS. Use Amazon QuickSight to visualize the results from RDS

4. Transform the data from JSON format to Apache Parquet format using an AWS Glue job. Configure AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Leverage Amazon Athena to create a table with a subset of columns. Set up Amazon QuickSight for visual analysis of the data and identify fraudulent transactions using QuickSight's built-in machine learning-powered anomaly detection

What's your guess? Scroll below for the answer...

Correct: 2.

Transform the data from JSON format to Apache Parquet format using an AWS Glue job. Configure AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Leverage Amazon Athena to create a table with a subset of columns. Set up Amazon QuickSight for visual analysis of the data and identify fraudulent transactions using QuickSight's built-in machine learning-powered anomaly detection

For the given use case, you can use an AWS Glue job to extract, transform, and load (ETL) data from the data source (in JSON format) to the data target (in Parquet format). You can then use an AWS Glue crawler, which is a program that connects to a data store (source or target) such as Amazon S3, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the AWS Glue Data Catalog.

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Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to set up or manage, and you pay only for the queries you run, thereby making this solution really low cost. You can also use Athena to build a table with only the subset of columns that are required for downstream analysis.

Finally, you can read the data in the given Athena table via Amazon QuickSight to identify fraudulent transactions using QuickSight's built-in machine learning-powered anomaly detection. QuickSight uses proven Amazon technology to continuously run ML-powered anomaly detection across millions of metrics to discover hidden trends and outliers in your data. This anomaly detection enables you to get deep insights that are often buried in the aggregates and not scalable with manual analysis. With ML-powered anomaly detection, you can find outliers in your data without the need for manual analysis, custom development, or ML domain expertise.

Incorrect options:

Leverage Kinesis Data Analytics to detect anomalies on a data stream from Kinesis Streams by running SQL queries which compute an anomaly score for all transactions and then store all fraudulent transactions in Amazon S3. Use Amazon QuickSight to visualize the results from Amazon S3 - Using Kinesis Data Analytics involves some custom query development to analyze the incoming data to compute an anomaly score for all transactions. In addition, this solution processes all columns of the data instead of just the subset of columns required for the analysis. Therefore, this option is not the best fit for the given use case.

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