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A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?
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C
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Explanation
The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 in Apache Parquet format. The most efficient solution, requiring the least manual effort, would be to use AWS Database Migration Service (DMS) for continuous data replication. Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manual transformations or separate jobs. This option is the least effort solution since it automates both the ingestion and transformation processes. Other options: Option A (Apache Sqoop on EMR) involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs. Option B (AWS Glue bookmark job) involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication. Option D (RDS and Lambda triggers) introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently. [References:, AWS Database Migration Service (DMS), DMS S3 Target Documentation, , ]
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A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?
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B
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Explanation
Problem Analysis: The company uses AWS Glue for ETL pipelines and requires automatic data quality checks during pipeline execution. The solution must integrate with existing AWS Glue pipelines and evaluate data quality rules based on predefined thresholds. Key Considerations: Ensure minimal implementation effort by leveraging built-in AWS Glue features. Use a standardized approach for defining and evaluating data quality rules. Avoid custom libraries or external frameworks unless absolutely necessary. Solution Analysis: Option A: SQL Transform Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule. Increases operational overhead and deviates from Glue's declarative approach. Option B: Evaluate Data Quality Transform with DQDL AWS Glue provides a built-in Evaluate Data Quality transform. Allows defining rules in Data Quality Definition Language (DQDL), a concise and declarative way to define quality checks. Fully integrated with Glue Studio, making it the least effort solution. Option C: Custom Transform with PyDeequ PyDeequ is a powerful library for data quality checks but requires custom code and integration. Increases implementation effort compared to Glue's native capabilities. Option D: Custom Transform with Great Expectations Great Expectations is another powerful library for data quality but adds complexity and external dependencies. Final Recommendation: Use Evaluate Data Quality transform in AWS Glue. Define rules in DQDL for checking thresholds, null values, or other quality criteria. This approach minimizes development effort and ensures seamless integration with AWS Glue. [:, AWS Glue Data Quality Overview, DQDL Syntax and Examples, Glue Studio Transformations, , ]
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A company has a data pipeline that uses an Amazon RDS instance, AWS Glue jobs, and an Amazon S3 bucket. The RDS instance and AWS Glue jobs run in a private subnet of a VPC and in the same security group.
A use' made a change to the security group that prevents the AWS Glue jobs from connecting to the RDS instance. After the change, the security group contains a single rule that allows inbound SSH traffic from a specific IP address.
The company must resolve the connectivity issue.
Which solution will meet this requirement?
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A
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Find answers to the most common questions about the Amazon Web Services Data-Engineer-Associate exam, including what it is, how to prepare, and how it can boost your career.
The Amazon Web Services Data-Engineer-Associate certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the Amazon Web Services Data-Engineer-Associate certification syllabus. The Amazon Web Services Data-Engineer-Associate certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in Amazon Web Services Data-Engineer-Associate certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, Amazon Web Services Data-Engineer-Associate certification may require periodic renewal to stay current with new innovations in the concerned domains.