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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Protect sensitive data - Enforce compliance and data governance
|
| Topic 2: Data Ingestion and Transformation | 34% | - Transform and enrich data
- Ingest data from various sources
|
| Topic 3: Data Store Management | 26% | - Design and implement data storage solutions
- Manage data lifecycle and storage tiers |
| Topic 4: Data Operations and Support | 22% | - Automate operational tasks - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery |
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NEW QUESTION # 127
A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?
Answer: C
Explanation:
To reclaim the most storage space from a materialized view in Amazon Redshift, you should use a DELETE operation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as 1=1. This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.
* Option A: DELETE FROM materialized_view_name WHERE 1=1;This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.
Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don't remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).
References:
* Amazon Redshift Materialized Views Documentation
* Deleting Data from Redshift
NEW QUESTION # 128
A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.
Answer: B
Explanation:
AWS Glue Workflows can coordinate multiple ETL jobs and triggers. They support parallel execution and sequential dependencies, which is ideal for concurrent data processing followed by correlation steps, all with minimal operational overhead.
"Use AWS Glue Workflows to orchestrate multiple ETL jobs in sequence or in parallel, supporting conditional triggers and dependency management."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 129
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?
Answer: B
Explanation:
The RootDiskUsed metric for the MSK cluster indicates that the storage on the broker is reaching its capacity. The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.
Expand MSK Broker Storage:
AWS Managed Streaming for Apache Kafka (MSK) allows you to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.
Reference:
Alternatives Considered:
B (Expand Zookeeper storage): Zookeeper is responsible for managing Kafka metadata and not for storing data, so increasing Zookeeper storage won't resolve the root disk issue.
C (Update instance type): Changing the instance type would increase computational resources but not directly address the storage problem.
D (Target-Volume-in-GiB): This parameter is irrelevant for the existing topic and will not solve the storage issue.
Amazon MSK Storage Auto Scaling
NEW QUESTION # 130
A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.
A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team's data warehouse. The operational Aurora database must not be affected.
Which solution will meet these requirements with the LEAST operational effort?
Answer: C
NEW QUESTION # 131
A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?
Answer: C
Explanation:
To reclaim the most storage space from a materialized view in Amazon Redshift, you should use a DELETE operation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as 1=1. This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.
Option A: DELETE FROM materialized_view_name WHERE 1=1;This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.
Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don't remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).
References:
Amazon Redshift Materialized Views Documentation
Deleting Data from Redshift
NEW QUESTION # 132
......
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