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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Choose a data store
|
| Topic 2: Data Ingestion and Transformation | 34% | - Perform data ingestion
|
| Topic 3: Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Topic 4: Data Security and Governance | 18% | - Implement data quality checks
|
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NEW QUESTION # 59
A company wants to build a dimension table in an Amazon S3 bucket. The bucket contains historical data that includes 10 million records. The historical data is 1 TB in size.
A data engineer needs a solution to update changes for up to 10,000 records in the base table every day.
Which solution will meet this requirement with the LOWEST runtime?
Answer: A
Explanation:
Option D provides the lowest runtime because it uses a table format designed for efficient incremental upserts on Amazon S3, rather than repeatedly scanning and rewriting large portions of a 1 TB dataset.
Although Spark on its own (Options A and C) can perform joins/merges, updating files stored in S3 typically requires expensive rewrites, especially as data grows. By contrast, Apache Hudi is purpose-built for maintaining large datasets on object storage with incremental updates, which directly fits "update up to
10,000 records every day" without reprocessing the full historical footprint.
For the compute layer, the document highlights that Amazon EMR provides a fully managed environment for running Apache Spark and other big data frameworks to process and analyze large datasets, making it appropriate for high-scale processing where performance matters. This is a better fit than using Pandas on 1 TB (Option B), which is not designed for distributed processing at that scale.
Therefore, combining EMR + Spark with an incremental storage framework (Hudi) is the most runtime-efficient approach for daily record-level updates on S3.
NEW QUESTION # 60
A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?
Answer: B
Explanation:
To avoid duplicate records in Amazon Redshift, the most effective solution is to perform the ETL in a way that first loads the data into a staging table and then uses SQL commands like MERGE or UPDATE to insert new records and update existing records without introducing duplicates.
* Using Staging Tables in Redshift:
* The AWS Glue job can write data to a staging table in Redshift. Once the data is loaded, SQL commands can be executed to compare the staging data with the target table and update or insert records appropriately. This ensures no duplicates are introduced during re-runs of the Glue job.
NEW QUESTION # 61
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon Redshift supportsdynamic data masking, which enables you to limit sensitive data visibility to specific users and roles without duplicating the data. This approach supports showing only parts of a column's values (e.g., last four digits) and full visibility for authorized roles (e.g., auditors).
"With dynamic data masking, you can control how much sensitive data a user sees in query results without changing the data in the table."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf IAM roles are used to associate users with the appropriate masking rules, keeping security tight and avoiding the creation of duplicate data views or tables.
NEW QUESTION # 62
A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets.
Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?
Answer: A
Explanation:
Option C is correct because Amazon Redshift data sharing is built for exactly this use case: providing live, immediate, read-only access to data across Redshift clusters and AWS accounts without copying or moving the data. AWS states that data sharing lets you share live data "without having to create a copy or move it," and that users can access the most up-to-date and consistent information as it is updated in the producer cluster. AWS also documents that data sharing provides instant, granular, and high-performance access across Amazon Redshift clusters and AWS accounts, which directly matches the requirement to preserve current Redshift performance. Since the downstream clusters use RA3 nodes, they satisfy the node-family requirement commonly associated with Redshift data sharing scenarios.
Option A would move the data to S3 and query it through Spectrum, which changes the performance profile and does not maintain native Redshift performance characteristics as well as direct Redshift data sharing.
Option B introduces delay and duplicated storage through ETL copies. Option D is not an appropriate method for synchronizing Redshift datasets between clusters. Therefore, Redshift data sharing is the most direct, immediate, and performance-preserving solution.
NEW QUESTION # 63
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on-premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes. AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog. You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.
The other options are not optimal for the following reasons:
A . Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
C . Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
D . Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers' ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.
Reference:
1: AWS Glue Data Catalog
2: AWS Glue Crawlers
: Amazon Aurora
: AWS Lambda
: Amazon DynamoDB
NEW QUESTION # 64
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