Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

The requirement to retain immutable object versions using a WORM model mandates the use of S3 Object Lock, not just S3 Versioning. Object Lock enforces immutability by preventing deletion or modification of objects for a specified retention period, which is required for compliance scenarios such as regulatory data retention.

To meet the access requirement of retrieving data within 5 hours, the appropriate archival storage class is S3 Glacier Flexible Retrieval, which supports expedited and standard retrieval within minutes to hours. S3 Glacier Deep Archive does not meet this requirement because retrieval times are typically 12 hours or longer.

Using S3 Intelligent-Tiering for active data optimizes storage costs automatically before transitioning older objects. A lifecycle policy can then transition objects older than 1 year to Glacier Flexible Retrieval and delete them after the 7-year retention period expires.

Options that rely only on S3 Versioning do not provide immutability guarantees. Options using Glacier Deep Archive fail the access-time requirement. Therefore, Option C is the only solution that satisfies WORM compliance, retrieval-time constraints, and cost optimization.

Problem Analysis:

The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).

The solution must avoid copying any data.

Key Considerations:

CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.

Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.

Solution Analysis:

Option A: Copies both schema and data. Does not meet the requirement for an empty table.

Option B: Inserts data into an existing table, which does not create a new table.

Option C: Creates an empty table but does not copy the schema.

Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.

Final Recommendation:

Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.

Athena CTAS Queries

CREATE TABLE Statement in Athena

 Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:

Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)

Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools)

Monitoring with AWS Glue job run insights (Section: Requirements)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)