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| Section | Weight | Objectives |
|---|---|---|
| Data Governance, Security & Sharing | 10% | - Track lineage, audit, and maintain data integrity - Apply access control, masking, encryption, and compliance - Share data securely across accounts and platforms |
| Data Ingestion | 25% | - Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Handle file formats, compression, encoding, and schema detection |
| Data Transformation & Processing | 30% | - Use streams, tasks, dynamic tables for pipelines and CDC - Work with semi-structured and unstructured data - Implement data quality, validation, and schema evolution - Design and implement transformations: SQL, stored procedures, user-defined functions |
| Performance Optimization & Scalability | 15% | - Monitor and tune workloads and resource usage - Optimize warehouses, clustering, partitioning, and query performance - Design scalable compute and storage solutions |
| Data Pipeline Orchestration & Automation | 20% | - Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows - Error handling, retry logic, and dependency management |
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NEW QUESTION # 84
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads. Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
AWS Glue is a fully managed, highly scalable serverless service for running PySpark ETL jobs, and it supports adding custom Python libraries to Glue jobs from Amazon S3 by using job parameters such as --additional-python-modules and --extra-py-files. AWS Glue can also write to Amazon Redshift, making it the best fit for daily large-scale PySpark transformations with the least operational overhead compared with managing EC2 instances or EMR clusters.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-libraries.html
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect-redshift- home.html
NEW QUESTION # 85
A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?
Answer: B
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html
NEW QUESTION # 86
A company uses an Amazon RDS for Oracle database to store data for an ecommerce application. The company needs a solution to make the data in all tables available for future analytics. The solution must process data from a table named customer_complaints in near real time by using a REST API.
Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
AWS DMS can stream change data from the Oracle customer_complaints table to Kinesis Data Streams for near real-time processing by a Lambda that calls the REST API, while a separate DMS task delivers all tables to Amazon S3 for future analytics. This achieves both streaming and batch needs with minimal infrastructure and cost.
NEW QUESTION # 87
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?
Answer: A
Explanation:
AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.
NEW QUESTION # 88
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte- scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html
Use the Query Result Reuse Feature of Amazon Athena. This leverages Athena's built-in feature to reduce redundant data scans and thus lowers query costs.
NEW QUESTION # 89
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