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

1. A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?

A) Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
B) Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
C) Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.
D) Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.

2. A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises servers are the same as the logs that are stored in the S3 bucket.
Which solution will meet this requirement?

A) After uploading the objects to the S3 bucket, enable S3 Object Lock in governance mode on the S3 objects.
B) Use the AWS SDK to automatically compute CRC32 checksums during the upload. Store the checksums in S3 object metadata.
C) Enable S3 Object Lock in compliance mode on the S3 bucket. Upload the objects to the bucket.
D) Create an AWS Lambda function to calculate SHA-256 checksums. Store the results in a separate metadata table. Validate the logs after the upload.

3. An ecommerce company collects daily customer transaction logs in CSV format and stores the logs in Amazon S3. The company uses Amazon Athena to scan a subset of attributes from the logs on the same day the company receives each log.
Query times are increasing because of increasing transaction volume. The company wants to improve query performance.
Which solution will meet these requirements with the SHORTEST query times?

A) Convert the CSV logs to JSON. Partition by date in Amazon S3. Use Athena with dynamic filtering to reduce data scans.
B) Convert the CSV logs to Avro. Partition by date in Amazon S3. Use Athena with projection-based partitioning.
C) Convert the CSV logs into multiple ORC files for better parallelism in Athena. Partition by date in Amazon S3. Use columnar pushdown filters.
D) Convert the CSV logs to a single Apache Parquet file for each day. Partition the data by date in Amazon S3. Use Athena with predicate pushdown filters.

4. A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand.
Which solution will meet these requirements?

A) Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
B) Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
C) Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
D) Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.

5. A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use an AWS Lambda function that includes both the business and the analytics logic to perform time- based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
B) Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
C) Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.
D) Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.

Solutions: