[RETIRED] Practice Exams | AWS Data Analytics Specialty

Based on the provided information, it seems you are looking to outline a step-by-step process for transforming JSON data into Apache Parquet format, crawling the data using AWS Glue, and then using Amazon Athena and Amazon QuickSight for analysis and fraud detection. Here's how you can achieve this:

1. Data Transformation with AWS Glue Job:

   • Create an AWS Glue job script to read from your JSON source in Amazon S3.
   • Transform the data into Apache Parquet format, which is efficient for analytics workloads.
   • Write the transformed data back to Amazon S3.

2. AWS Glue Crawler Configuration:

   • Configure an AWS Glue crawler to connect to the location where your Parquet files are stored.
   • The crawler will automatically infer the schema of the Parquet files and create a table definition in the AWS Glue Data Catalog.

3. Create an Athena Table:

   • Use the CREATE EXTERNAL TABLE SQL statement in Amazon Athena to define a table with the desired subset of columns from your data.
   • Specify the location of your data in S3 and the format (Apache Parquet).

4. Visual Analysis with Amazon QuickSight:

   • Connect Amazon QuickSight to your Athena table.
   • Use QuickSight's built-in machine learning-powered anomaly detection to identify fraudulent transactions without manual analysis or custom development.

5. Fraud Detection:

   • QuickSight automatically models what is "normal" for the data and then highlights anomalies, making it easy to spot potential fraudulent activities.
   • Explore the visualizations provided by QuickSight to understand patterns, trends, and anomalies in your data.

Incorrect options provided:

• Kinesis Data Analytics: This service is used for real-time stream processing using SQL queries, not for batch analytics on Parquet files or fraud detection directly.

• Amazon SageMaker: While it's a powerful tool for building machine learning models, it requires custom code development and is not the same as using QuickSight's built-in anomaly detection feature.

• Kinesis Data Firehose + Lambda + Amazon RDS: This combination is not directly relevant to the use case of transforming JSON data into Parquet format for analysis and fraud detection in the AWS ecosystem. Lambda would be used for real-time processing, not batch transformation and analysis.

The course by instructors Stéphane Maarek and Abhishek Singh offers practice exams for the AWS Certified Data Analytics Specialty exam, with a focus on helping learners understand the concepts and prepare effectively. The course includes a large question bank, support from instructors, detailed explanations, mobile compatibility with the Udemy app, and a money-back guarantee if unsatisfied.

Remember, the AWS Certified Data Analytics Specialty exam validates your expertise in designing and managing scalable data analytics solutions on AWS. Good luck with your preparation and exam!