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

1. A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company ' s data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

A) Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
B) Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
C) Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.
D) Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database. Configure the Sqoop job to write the data to Amazon S3 in Parquet format.

2. A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.

A) Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.
B) Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.
C) Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.
D) Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.

3. A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company ' s operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?

A) Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows
B) AWS Glue workflows
C) AWS Lambda functions
D) AWS Step Functions tasks

4. A company uses an Amazon QuickSight dashboard to monitor usage of one of the company ' s applications.
The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

A) Convert the AWS Glue schema to the DynamicFrame schema class.
B) Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
C) Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
D) Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.
E) Increase the AWS Glue instance size by scaling up the worker type.

5. A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)

A) Split the data into files that are less than 10 KB.
B) Use file formats that are not
C) Partition the data based on the most common query predicates.
D) Use a columnar storage file format.
E) Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.

Solutions: