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

1. A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?

A) Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
B) Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
C) Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
D) Use Amazon Athena Federated Query to join the data from all data sources.

2. A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company ' s analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

A) Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
B) Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.
C) Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
D) Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.

3. A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake.
Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use the AWS Glue API to manually update the Data Catalog.
B) Schedule an AWS Glue crawler to periodically update the Data Catalog.
C) Run an MSCK REPAIR TABLE command in Athena.
D) Run a REFRESH TABLE command in Athena.

4. 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.

5. An ecommerce company stores sales data in an AWS Glue table named sales_data. The company stores the sales_data table in an Amazon S3 Standard bucket. The table contains columns named order_id, customer_id, product_id, order_date, shipping_date, and order_amount.
The company wants to improve query performance by partitioning the sales_data table by order_date. The company needs to add the partition to the existing sales_data table in AWS Glue.
Which solution will meet these requirements?

A) Use the AWS Glue Data Catalog API to add the new partition to the table.
B) Update the AWS Glue table's schema to include the new partition.
C) Manually modify the S3 bucket to use the new partition.
D) Edit the AWS Glue table's metadata file directly in Amazon S3.

Solutions: