DEA-C01 Practice Test 27 — 15 Questions

Question 1

A data engineer is migrating an on-premises PostgreSQL database to Amazon RDS for PostgreSQL. The database is 2 TB in size and has a tight migration window. Which migration approach minimizes downtime?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use AWS DMS with ongoing replication to capture changes during migration.. AWS DMS with ongoing replication (change data capture, CDC) allows you to perform a full load of the existing data and then continuously replicate incremental changes from the source PostgreSQL database to the target RDS for PostgreSQL. This minimizes downtime because you can keep the source database fully operational during the migration and only switch applications over after the target is fully synchronized, resulting in a very short cutover window.

Answer

Use AWS Database Migration Service (DMS) with full load only.

Answer

Use pg_dump to export the database and pg_restore to import into RDS.

Answer

Create a read replica in RDS and promote it when ready.

Question 2

A company uses AWS Lake Formation to manage data lake permissions. The data lake contains sensitive customer data in the 'customer' database. The security team wants to ensure that only users with a specific tag 'access_level=analyst' can query the 'customer' table. Which combination of steps should the data engineer take to enforce this?

Accepted Answer

In Lake Formation, create an LF-tag 'access_level' with values 'analyst' and 'admin'. Grant 'SELECT' permission on the 'customer' table to the tag value 'analyst'. Associate the LF-tag with the 'customer' table.. Lake Formation LF-tags allow you to define metadata tags (key-value pairs) and grant permissions to those tags. By creating an LF-tag 'access_level' with values 'analyst' and 'admin', granting SELECT on the 'customer' table to the tag value 'analyst', and associating that LF-tag with the table, only principals who have the tag 'access_level=analyst' (or are granted via the tag) can query the table. This enforces tag-based access control at the Lake Formation permission layer, which is the intended mechanism for fine-grained, attribute-based access control in Lake Formation.

Answer

Create an IAM policy that conditionally allows 'glue:GetTable' based on the tag 'access_level=analyst'.

Answer

Apply a bucket policy on the S3 location of the 'customer' table that allows access only if the request carries the tag 'access_level=analyst'.

Answer

Use Lake Formation column-level filters to restrict access to columns based on the tag 'access_level=analyst'.

Question 3

A data engineer needs to ingest data from an on-premises Oracle database into Amazon S3. The data volume is about 500 GB initially, with daily incremental updates of 10 GB. The pipeline must minimize operational overhead. Which AWS service should be used for the initial and incremental loads?

Accepted Answer

AWS Database Migration Service (DMS) with change data capture (CDC) to Amazon S3.. AWS DMS with CDC is the correct choice because it supports continuous replication from Oracle to Amazon S3 with minimal overhead. It handles both the initial 500 GB full load and ongoing 10 GB daily increments via change data capture, without requiring custom code or complex pipeline management.

Answer

AWS Glue with a JDBC connection and incremental crawl.

Answer

Amazon Kinesis Data Firehose with a custom producer.

Answer

AWS Data Pipeline with a SQL activity and HiveCopyActivity.

Question 4

A company uses Amazon S3 to store large datasets. The data engineering team needs to provide access to specific objects in the bucket to external partners using presigned URLs. Each URL should expire after 12 hours. The team wants to ensure that the presigned URLs cannot be used to access other objects in the bucket. Which approach should be taken?

Accepted Answer

Generate presigned URLs using the AWS SDK, specifying the exact object key and expiration time.. Presigned URLs generated via the AWS SDK allow you to specify the exact object key and expiration time, ensuring that the URL grants access only to that specific object for the defined 12-hour period. This approach uses the secret key of the IAM user or role to sign the URL, and the signature is tied to the object key, so the URL cannot be reused to access other objects in the bucket.

Answer

Create an IAM role for each partner and attach a policy that grants access to specific objects.

Answer

Use a bucket policy that allows access only from the partner's IP address range.

Answer

Use CloudFront signed URLs with a custom policy that restricts access to specific objects.

Question 5

A company needs to ingest data from multiple SaaS applications into Amazon S3. The data sources provide REST APIs. Which AWS service can be used to build a fully managed data ingestion pipeline without writing custom code?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is a fully managed service designed to transfer data from SaaS applications to AWS services like Amazon S3 without writing custom code. Option B is wrong because Amazon Kinesis Data Streams is primarily for real-time streaming data, not for directly ingesting from SaaS APIs. Option C is wrong because AWS Lambda requires custom code to integrate with SaaS APIs. Option D is wrong because AWS Glue with Python shell is for ETL transformations and also requires custom scripting.

Answer

Amazon Kinesis Data Streams

Answer

AWS Lambda with custom code

Answer

AWS Glue with Python shell

Question 6

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format and needs to be converted to Parquet. However, the conversion is failing. What is the most likely cause?

Accepted Answer

The schema is not defined in the AWS Glue Data Catalog. Kinesis Data Firehose requires a schema to convert to Parquet. This schema can be provided by a Glue Data Catalog table. If the schema is not defined, the conversion fails. Data size is not an issue for conversion. Kinesis Data Streams is not involved here. Lambda transformation can convert to Parquet but is not required.

Answer

The data transformation Lambda function is not converting to Parquet

Answer

The data size exceeds the 1 MB limit per record

Answer

The delivery stream is configured to use Kinesis Data Streams as source

Question 7

A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is consumed by an AWS Lambda function that writes to Amazon DynamoDB. The Lambda function is seeing high error rates due to DynamoDB write throttling. Which action should be taken to reduce throttling?

Accepted Answer

Enable auto scaling on the DynamoDB table. Enabling DynamoDB auto scaling increases write capacity automatically when needed. Using Kinesis Data Firehose would change the architecture but does not address throttling directly. Increasing Lambda memory does not help with DynamoDB throttling. Using SQS would add a queue but does not increase DynamoDB capacity.

Answer

Use Amazon Kinesis Data Firehose instead of Kinesis Data Streams

Answer

Add an Amazon SQS queue between Lambda and DynamoDB

Answer

Increase the Lambda function memory

Question 8

A company needs to ingest data from a MySQL database into Amazon S3 using AWS DMS. The data changes frequently and the requirement is to capture changes in near real-time. Which THREE configurations are necessary?

Accepted Answer

Create an S3 target endpoint in DMS.. To capture near real-time changes from a MySQL database to S3 using AWS DMS, you need: B) an S3 target endpoint in DMS to write the data; C) binary logging (binlog) enabled on MySQL to capture changes; and D) a DMS replication instance to process and transfer data. Options A and E are unnecessary: a VPC endpoint for S3 is not required (DMS can use public endpoints or VPC peering), and S3 event notifications are not used to trigger DMS—they are typically for post-processing.

Answer

Create a VPC endpoint for S3.

Answer

Configure an S3 event notification to trigger DMS.

Question 9

A company needs to ingest data from an on-premises Oracle database into Amazon S3 on a daily basis. The data volume is about 100 GB per day. Which AWS service is BEST suited for this task?

Accepted Answer

Use AWS Database Migration Service (DMS) to replicate data to S3.. AWS Database Migration Service (DMS) can continuously replicate data from Oracle to S3, and it supports full load and change data capture (CDC). Option A (AWS DataSync) is for file-based transfers, not database replication. Option B (Amazon Kinesis Data Firehose) is for streaming data, not database pull. Option D (AWS Glue) is for ETL but does not natively support continuous CDC from Oracle.

Answer

Use AWS DataSync to copy the database files to S3.

Answer

Use Amazon Kinesis Data Firehose with a database connector.

Answer

Use AWS Glue to extract data from Oracle and write to S3.

Question 10

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is delivered in JSON format. The company wants to convert the data to Apache Parquet format before delivery to reduce storage costs and improve query performance. How can this be achieved?

Accepted Answer

Use the AWS Glue Data Catalog to define a schema and configure Firehose to use it for Parquet conversion.. Kinesis Data Firehose can convert the input data to Parquet or ORC format using a schema from the AWS Glue Data Catalog. Option A is incorrect because delivering as JSON then converting with Athena is an extra step after storage, not before delivery, and does not reduce storage costs from the outset. Option C is incorrect because Lambda can be used for custom transformations, but Firehose natively supports Parquet conversion using Glue. Option D is incorrect because Firehose cannot convert to Parquet automatically without a schema; you must provide a schema (e.g., from Glue).

Answer

Deliver data to S3 as JSON, then use Amazon Athena to convert to Parquet.

Answer

Write an AWS Lambda function to transform the data to Parquet and deliver it to S3.

Answer

Configure the Firehose stream to convert data to Parquet automatically without any additional setup.

Question 11

A data engineer is designing a data ingestion pipeline for JSON files landing in an Amazon S3 bucket. The pipeline must transform the data (e.g., flatten nested structures) and load it into Amazon Redshift. The transformation logic is complex and may evolve frequently. Which approach provides the MOST flexibility and ease of maintenance?

Accepted Answer

Use AWS Glue ETL jobs to transform the data and load into Redshift.. AWS Glue ETL jobs provide a serverless, code-based environment using Apache Spark, which offers flexibility for complex transformations like flattening nested JSON structures. Glue can be configured to support exactly-once semantics through Spark checkpointing and transactional writes to Redshift, making it reliable for critical data pipelines. It handles varying file sizes and can be easily updated as transformation logic evolves. Option A is incorrect because Lambda has execution time and memory limits, making it unsuitable for large JSON files or complex transformations, and achieving exactly-once requires careful idempotency design. Option B is incorrect because the Redshift COPY command loads raw JSON without transformation. Option D is incorrect because Athena is primarily for querying data in S3, not for performing ETL transformations and loading into Redshift.

Answer

Use AWS Lambda functions to transform each file and load into Redshift.

Answer

Use the Amazon Redshift COPY command to load raw JSON directly.

Answer

Use Amazon Athena to query the raw data and insert into Redshift.

Question 12

A company wants to ingest streaming data from social media feeds into AWS for real-time analytics. Which TWO services can directly ingest streaming data without writing custom code? (Choose TWO.)

Accepted Answer

Amazon AppFlow. Amazon Kinesis Data Firehose can directly ingest streaming data and deliver to destinations. AWS Glue can stream from Kafka but not directly ingest from social media without custom connectors. Kinesis Data Streams requires producers to send data, not direct ingestion. AppFlow can ingest from SaaS applications including social media.

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Answer

Amazon S3 Transfer Acceleration

Question 13

Refer to the exhibit. A data engineer runs this CLI command on an S3 bucket. The data is ingested from multiple sources. Which AWS service would be best to process these files in a single batch transformation?

Accepted Answer

AWS Glue. AWS Glue is designed for batch ETL processing, capable of handling multiple files of varying sizes in a single transformation job. Option A is wrong because Lambda has limitations on execution time (15 minutes) and memory (10 GB), making it unsuitable for large-scale batch transforms. Option B is wrong because Kinesis Data Analytics is for real-time stream processing, not batch. Option C is wrong because Athena is an interactive query service for ad-hoc analysis of data in S3, not for batch transformations.

Answer

AWS Lambda

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Athena

Question 14

A data engineer is designing a data ingestion pipeline for real-time clickstream data using Amazon Kinesis Data Streams. The data must be transformed using AWS Lambda and then stored in Amazon S3 in Parquet format. Which Kinesis client library configuration should be used to minimize the number of Lambda invocations while ensuring data is processed within 60 seconds?

Accepted Answer

Set batch size to 10000 records and batch window to 60 seconds. A batch size of 10,000 records combined with a batch window of 60 seconds maximizes the number of records per Lambda invocation while respecting the 60-second processing requirement, thereby minimizing the total number of invocations. Option A (batch size 100, no batch window) leads to many small invocations. Option C (batch window 0 seconds) triggers immediate processing, increasing invocation frequency. Option D (batch window 5 seconds) forces more frequent invocations than necessary, even with a large batch size.

Answer

Set batch size to 100 records and disable batch window

Answer

Set batch size to 100 records and batch window to 0 seconds

Answer

Set batch size to 10000 records and batch window to 5 seconds

Question 15

A company uses AWS DMS to continuously replicate data from an on-premises SQL Server to Amazon Aurora MySQL. The replication lag is increasing. Which THREE actions can reduce the lag? (Choose three.)

Accepted Answer

Use parallel apply on the target endpoint.

Answer

Enable DMS validation.

Answer

Enable Multi-AZ for the DMS replication instance.