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Data-Engineer-Associate Exam Dumps - AWS Certified Data Engineer - Associate (DEA-C01)

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Question # 4

A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.

The data engineer must identify and remove duplicate information from the legacy application data.

Which solution will meet these requirements with the LEAST operational overhead?

A.

Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importing the Pandas library to perform data deduplication.

B.

Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.

C.

Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

D.

Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

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Question # 5

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 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.

B.

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.

C.

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.

D.

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.

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Question # 6

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?

A.

Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.

B.

Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

C.

Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.

D.

Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.

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Question # 7

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.

A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.

Which solution will meet these requirements with the LEAST latency?

A.

Schedule an AWS Glue crawler to run every morning.

B.

Manually run the AWS Glue CreatePartition API twice each day.

C.

Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call.

D.

Run the MSCK REPAIR TABLE command from the AWS Glue console.

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Question # 8

A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?

A.

Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AW5 Glue for each day's data.

B.

Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

C.

Reduce the number of partitions by changing the partitioning schema from dairy to monthly granularity.

D.

Increase the processing capacity of Athena queries by allocating more compute resources.

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