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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q222-Q227):

NEW QUESTION # 222
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A. AWS Direct Connect
B. Amazon S3 Transfer Acceleration
C. AWS Glue
D. AWS DataSync

Answer: D

Explanation:
AWS DataSync is an online data movement and discovery service that simplifies and accelerates data migrations to AWS as well as moving data to and from on-premises storage, edge locations, other cloud providers, and AWS Storage services1. AWS DataSync can copy data to and from various sources and targets, including Amazon S3, and handle files in multiple formats. AWS DataSync also supports incremental transfers, meaning it can detect and copy only the changes to the data, reducing the amount of data transferred and improving the performance. AWS DataSync can automate and schedule the transfer process using triggers, and monitor the progress and status of the transfers using CloudWatch metrics and events1.
AWS DataSync is the most operationally efficient way to transfer the data in this scenario, as it meets all the requirements and offers a serverless and scalable solution. AWS Glue, AWS Direct Connect, and Amazon S3 Transfer Acceleration are not the best options for this scenario, as they have some limitations or drawbacks compared to AWS DataSync. AWS Glue is a serverless ETL service that can extract, transform, and load data from various sources to various targets, including Amazon S32. However, AWS Glue is not designed for large-scale data transfers, as it has some quotas and limits on the number and size of files it can process3. AWS Glue also does not support incremental transfers, meaning it would have to copy the entire data set every time, which would be inefficient and costly.
AWS Direct Connect is a service that establishes a dedicated network connection between your on-premises data center and AWS, bypassing the public internet and improving the bandwidth and performance of the data transfer. However, AWS Direct Connect is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS DataSync, AWS Storage Gateway, or AWS CLI. AWS Direct Connect also has some hardware and location requirements, and charges you for the port hours and data transfer out of AWS.
Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to Amazon S3 over long distances, using the AWS edge locations and optimized network paths. However, Amazon S3 Transfer Acceleration is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS CLI, AWS SDK, or third-party software. Amazon S3 Transfer Acceleration also charges you for the data transferred over the accelerated endpoints, and does not guarantee a performance improvement for every transfer, as it depends on various factors such as the network conditions, the distance, and the object size. Reference:
AWS DataSync
AWS Glue
AWS Glue quotas and limits
[AWS Direct Connect]
[Data transfer options for AWS Direct Connect]
[Amazon S3 Transfer Acceleration]
[Using Amazon S3 Transfer Acceleration]

NEW QUESTION # 223
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. Option D
B. Option C
C. Option A
D. Option B

Answer: C

Explanation:
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
* Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of
'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
References:
* Amazon Athena SQL Reference
* Creating Tables in Athena

NEW QUESTION # 224
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 Lake Formation FindMatches transform in the ETL job.
C. Train and use the AWS Glue PySpark Filter class in the ETL job.
D. Use Amazon Made pattern matching as part of the ETL job.

Answer: B

Explanation:
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake FormationFindMatchesis 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:
* FindMatchesis 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.
Reference:AWS Lake Formation FindMatches
Alternatives Considered:
A (Amazon Made pattern matching): Amazon Made is not a service in AWS, and pattern matching typically refers to regular expressions, which are not suitable for deduplication without a common identifier.
B (AWS Glue PySpark Filter class): PySpark's Filter class can help refine datasets, but it does not offer the ML-based matching capabilities required to find matches between records without unique identifiers.
C (Partition tables on a unique identifier): Partitioning requires a unique identifier, which the question states is unavailable.
References:
AWS Glue Documentation on Lake Formation FindMatches
FindMatches in AWS Lake Formation

NEW QUESTION # 225
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?

A. Develop an AWS Glue Python job to read the historical data and new changes into two Pandas DataFrames. Use the Pandas update method to update the base table.
B. Develop an Amazon EMR job to read new changes into Apache Spark DataFrames. Use the Apache Hudi framework to create the base table in Amazon S3. Use the Spark update method to update the base table.
C. Develop an AWS Glue Apache Spark job to read the historical data and new changes into two Spark DataFrames. Use the Spark update method to update the base table.
D. Develop an Apache Spark job in Amazon EMR to read the historical data and the new changes into two Spark DataFrames. Use the Spark update method to update the base table.

Answer: B

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 # 226
A data engineer needs to query data from multiple sources to generate an annual report. The analytics team uses Amazon Redshift for analysis. The data engineer needs to integrate Amazon Redshift data with 10 years of historical data from Amazon RDS for PostgreSQL and RDS for MySQL. All the databases are in the same VPC. The data engineer needs a solution that provides seamless data integration with Amazon Redshift.
Which solution will meet these requirements in the MOST cost-effective way?

A. Use AWS Database Migration Service (AWS DMS) to ingest data from RDS for PostgreSQL and RDS for MySQL. Implement the necessary transformations within Amazon Redshift.
B. Create a visual extract, transform, and load (ETL) job in AWS Glue to extract the required data and load it to Amazon Redshift.
C. Use federated queries in Amazon Redshift to fetch data from RDS for PostgreSQL and RDS for MySQL. Apply the necessary transformations within Amazon Redshift.
D. Use the SELECT INTO OUTFILE S3 statement to export data from Amazon RDS to Amazon S3. Use the COPY command to load the data into Amazon Redshift.

Answer: C

Explanation:
Option A is the most cost-effective because it enables seamless integration by allowing analysts to access and join external relational data directly from within Amazon Redshift for reporting, without building and operating a separate ingestion pipeline for a one-time (annual) workload. The study material frames Amazon Redshift as the centralized analytics store for complex queries and reporting, making it the natural place to perform the final transformations and analysis.
The alternatives introduce extra operational steps and recurring costs. Option B requires exporting large historical datasets to S3 and then loading them into Redshift, which increases data movement and operational complexity. Option C adds ETL job development, scheduling, retries, and monitoring overhead that is unnecessary if the primary goal is integrated querying for an annual report. Option D (DMS) is best when you need ongoing migration or continuous replication; it is typically more operationally heavy than needed for
"query across sources" use cases and still requires additional setup and maintenance.
Because all databases are already in the same VPC and the analytics platform is Redshift, federated querying provides the most direct, lowest-operations path to integrate and analyze the data where it already needs to be consumed.

NEW QUESTION # 227
......

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