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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q141-Q146):

NEW QUESTION # 141
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?

A. Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.
B. Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.
C. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
D. Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: C

Explanation:
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.
Amazon Athena Federated Queries:
Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.
Reference:
Alternatives Considered:
A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.
C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.
D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.
Amazon Athena Documentation
Federated Queries in Amazon Athena

NEW QUESTION # 142
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?

A. Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.
B. Use the DynamoDB TTL feature to automatically expire data based on timestamps.
C. Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.
D. Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.

Answer: B

Explanation:
The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).
Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.
DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.
Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.
Reference:
DynamoDB Time-to-Live (TTL)

NEW QUESTION # 143
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?

A. Partition tables and use the ETL job to partition the data on a unique identifier.
B. Train and use the AWS Glue PySpark Filter class in the ETL job.
C. Use Amazon Made pattern matching as part of the ETL job.
D. Train and use the AWS Lake Formation FindMatches transform in the ETL job.

Answer: D

Explanation:
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake Formation FindMatches is designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.
* D. Train and use the AWS Lake Formation FindMatches transform in the ETL job:
* FindMatches is a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier.
* It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks.
* FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.

NEW QUESTION # 144
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A. Choice state
B. Wait state
C. Map state
D. Parallel state

Answer: C

Explanation:
Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.
Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.
Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.
Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.
Reference:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10 AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3

NEW QUESTION # 145
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional dat a. The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Select TWO.)

A. Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.
B. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.
C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift duster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.
D. Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.
E. Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.

Answer: A,B

Explanation:
The goal is to archive historical data from an Amazon Redshift data warehouse while combining live transactional data from Amazon Aurora PostgreSQL with current and historical data in a cost-efficient manner. The company wants to keep only the last 15 months of data in Redshift to reduce costs.
Option A: "Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database." Redshift Federated Query allows querying live transactional data directly from Aurora PostgreSQL without having to move it into Redshift, thereby enabling seamless integration of the current data in Redshift and live data in PostgreSQL. This is a cost-effective approach, as it avoids unnecessary data duplication.
Option C: "Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3." This option uses Amazon Redshift Spectrum, which enables Redshift to query data directly in S3 without moving it into Redshift. By unloading older data (older than 15 months) to S3, and then using Spectrum to access it, this approach reduces storage costs significantly while still allowing the data to be queried when necessary.
Option B (Redshift Spectrum for live PostgreSQL data) is not applicable, as Redshift Spectrum is intended for querying data in Amazon S3, not live transactional data in Aurora.
Option D (S3 Glacier Flexible Retrieval) is not suitable because Glacier is designed for long-term archival storage with infrequent access, and querying data in Glacier for analytics purposes would incur higher retrieval times and costs.
Option E (materialized views) would not meet the need to archive data or combine it from multiple sources; it is best suited for combining frequently accessed data already in Redshift.
Reference:
Amazon Redshift Federated Query
Amazon Redshift Spectrum Documentation
Amazon Redshift UNLOAD Command

NEW QUESTION # 146
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Data-Engineer-Associate Exam Demo | Exam Data-Engineer-Associate Syllabus

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q141-Q146):

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