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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
| Data Security and Governance | 18% | - Implement data quality checks
|
| Data Store Management | 26% | - Understand data cataloging
|
| Data Operations and Support | 22% | - Automate data pipelines
|
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NEW QUESTION # 94
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
* Problem Analysis:
* The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.
* The goal is to analyze and correlate the two datasets with low operational overhead.
* The process must run once nightly.
* Key Considerations:
* Amazon AppFlow simplifies data integration with Salesforce.
* AWS Glue can extract data from MySQL and perform ETL operations.
* Step Functions can orchestrate workflows with minimal manual intervention.
* Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.
* Solution Analysis:
* Option A: MWAA + Lambda + Step Functions
* Requires custom Lambda code for dataset correlation, increasing development and operational complexity.
* Option B: AppFlow + Glue + MWAA
* MWAA adds orchestration overhead compared to the simpler Step Functions.
* Option C: AppFlow + Glue + Step Functions
* AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.
* Minimal setup and operational overhead, making it the best choice.
* Option D: AppFlow + Kinesis + Flink + Step Functions
* Using Kinesis and Flink for batch processing introduces unnecessary complexity.
* Final Recommendation:
* Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.
:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions
NEW QUESTION # 95
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team.
The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)
Answer: C,E
Explanation:
The company is using a data mesh architecture with AWS Lake Formation for governance and needs to share specific subsets of data with different teams (marketing and compliance) using Amazon Redshift Serverless.
* Option A: Create views of the tables that need to be shared. Include only the required columns.
Creating views in Amazon Redshift that include only the necessary columns allows for fine-grained access control. This method ensures that each team has access to only the data they are authorized to view.
* Option E: Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.Amazon Redshift data sharing enables live access to data across Redshift clusters or Serverless workgroups. By sharing data with specific workgroups, you can ensure that the marketing team and compliance team each access the relevant subset of data based on the views created.
* Option B (creating a Redshift data share) is close but does not address the fine-grained column-level access.
* Option C (creating a managed VPC endpoint) is unnecessary for sharing data with specific teams.
* Option D (sharing with the Lake Formation catalog) is incorrect because Redshift data shares do not integrate directly with Lake Formation catalogs; they are specific to Redshift workgroups.
References:
* Amazon Redshift Data Sharing
* AWS Lake Formation Documentation
NEW QUESTION # 96
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?
Answer: A
Explanation:
Option A is the best answer because AWS DMS documentation recommends that, when practical, you create the replication instance in the same Region as your target endpoint, and in the same VPC or subnet as your target endpoint. In this scenario, the target is the Amazon Redshift cluster in eu-west-1 in Account_B, so placing the replication instance there is the most appropriate design. AWS DMS uses the replication instance to connect to the source, read the source data, transform it as needed, and load it into the target.
This also makes architectural sense because the replication instance must have network connectivity to both endpoints, and colocating it with the Redshift target usually simplifies connectivity to the target warehouse and aligns with AWS guidance. A replication instance in Account_B, eu-west-1 can still connect to the PostgreSQL source in Account_A, eu-east-1, provided the required networking and permissions are configured. AWS DMS supports replication tasks by defining a replication instance plus source and target endpoints.
The study guide also identifies AWS DMS as the correct managed service for database migration and continuous replication scenarios, including full load and change data capture. That matches this cross-account, cross-Region migration use case.
NEW QUESTION # 97
A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements?
Answer: A
Explanation:
Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost- effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance. To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.
The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewrites the table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows. This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.
The other options are not optimal for the following reasons:
* A. VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.
* B. VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.
* D. VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order.Moreover, this option does not analyze the sort key column and update the table statistics.
:
1: Amazon Redshift VACUUM
2: Amazon Redshift Interleaved Sorting
3: Amazon Redshift ANALYZE
NEW QUESTION # 98
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Answer: C
Explanation:
AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3.
You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.
"Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort.
Automate validation by triggering jobs through EventBridge."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
NEW QUESTION # 99
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