DEA-C01 Data Ingestion and Transformation Practice Test 42 — 15 Questions

Question 1

A company needs to ingest data from a relational database into Amazon S3 for analytics. The database is an Amazon RDS MySQL instance. Which AWS service should be used for a one-time historical data load?

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

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is designed for migrating data from relational databases like Amazon RDS MySQL into targets such as Amazon S3, supporting one-time full loads as well as ongoing replication. For a one-time historical data load, DMS can extract the existing data and land it in S3 efficiently. This makes DMS the correct service for the described scenario.

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AWS Glue ETL

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Amazon Athena

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Amazon Kinesis Data Firehose

Question 2

A company is using Amazon Kinesis Data Streams with a Lambda consumer to process clickstream data. The data rate is high and the Lambda function is falling behind, resulting in increased processing latency. What is the MOST effective way to improve throughput?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Increasing the number of shards in the Kinesis stream increases the stream's total read capacity, allowing more concurrent Lambda invocations to process records in parallel. Since each shard supports up to 5 read transactions per second and a maximum of 2 MB/s read throughput, adding shards directly raises the aggregate throughput, enabling the Lambda consumer to keep up with the high data rate.

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Increase the memory allocated to the Lambda function.

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Increase the Lambda function timeout.

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Use Kinesis Data Firehose instead of Lambda.

Question 3

A company needs to ingest streaming data from multiple sources and store it in Amazon S3. The data volume is up to 5 GB per hour. What is the MOST cost-effective ingestion service?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the most cost-effective service for ingesting streaming data into Amazon S3 at 5 GB/hour. It is fully managed, automatically scales, and charges only for data ingested (per GB), with no upfront provisioning. While Amazon Kinesis Data Streams (KDS) may have lower throughput cost for steady loads, it requires manual shard management and typically needs additional components (e.g., Lambda functions) to deliver data to S3, increasing operational overhead and total cost. AWS Glue is a batch ETL service, not designed for streaming. AWS Lambda is a compute service and would require custom code and scaling logic, making it more expensive and complex for this use case. Therefore, Kinesis Data Firehose provides the simplest and most cost-effective solution for streaming data ingestion directly to S3.

Answer

AWS Glue

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Amazon Kinesis Data Streams

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AWS Lambda

Question 4

A company is using AWS Glue ETL to transform and load data from Amazon S3 to Amazon Redshift. The data engineer notices that the job is taking longer than expected. Which TWO actions can improve the job performance?

Accepted Answer

Partition the source data in S3.. Options B and C are correct because partitioning the source data in S3 reduces the amount of data scanned by Glue, improving I/O efficiency, and increasing the number of DPUs adds more parallelism for transformations. Option A is incorrect because Redshift Spectrum is for querying data in S3 directly from Redshift, not for Glue ETL jobs. Option D is incorrect because S3 Transfer Acceleration speeds up uploads to S3 but does not affect Glue job performance during ETL processing. Option E is incorrect because larger Redshift node types do not impact Glue job execution; they only affect Redshift query performance.

Answer

Use Amazon Redshift Spectrum to query data directly.

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Enable S3 Transfer Acceleration.

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Use a larger Redshift node type.

Question 5

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.. Option B is correct because AWS DMS requires an explicitly defined target endpoint that describes the S3 bucket, folder, and IAM role used to write the migrated data. Option C is correct because ongoing change data capture (CDC) from MySQL relies on the source's binary log (binlog), which must be enabled with settings such as log_bin and binlog_format=ROW so DMS can read committed changes in near real-time. Option D is correct because a replication instance is the managed compute resource that runs the DMS migration and CDC tasks between the source and target endpoints. Option A is not required, since a VPC endpoint for S3 is only an optional networking choice and not a mandatory DMS configuration. Option E is not required, because DMS tasks are started and monitored by DMS itself, not by S3 event notifications.

Answer

Create a VPC endpoint for S3.

Answer

Configure an S3 event notification to trigger DMS.

Question 6

A company is building a data lake on Amazon S3 and needs to ingest data from various on-premises sources. Which TWO AWS services can be used to transfer data securely over the internet?

Accepted Answer

AWS DataSync. AWS DataSync (B) is correct because it is a purpose-built data transfer service that moves data between on-premises storage and Amazon S3 over the internet using TLS encryption, and it supports scheduled, incremental, and bandwidth-limited transfers. AWS CLI (E) is correct because commands such as aws s3 cp or aws s3 sync transfer data from on-premises systems to S3 over the public internet using HTTPS, making it a valid secure internet-based ingestion method. AWS Snowcone (A) is not correct here because it is an edge computing and offline/physical data transfer device, not an internet-based transfer service. Amazon Kinesis Data Firehose (C) is not correct because it delivers streaming data to destinations like S3 but is not designed to pull files from on-premises sources over the internet. AWS Direct Connect (D) is not correct because it uses a dedicated private network connection rather than the public internet.

Answer

AWS Snowcone

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Amazon Kinesis Data Firehose

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AWS Direct Connect

Question 7

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time using custom Python code before being stored in Amazon S3. Which AWS service should be used to perform this transformation?

Accepted Answer

Kinesis Data Analytics for Apache Flink. Amazon EMR with Spark Streaming, which is optimized for large-scale batch and stream processing but is not the simplest or most direct service for this specific use case. Option B is AWS Lambda, which can be used for simple transformations but has limitations on execution time and complexity. Option D is Kinesis Data Firehose with custom data transformation, which supports only built-in transformations or Lambda functions, not arbitrary custom Python code directly. Option C, Kinesis Data Analytics for Apache Flink, is correct because it allows running custom Apache Flink applications, which support custom Python code via the Apache Flink Python API, for real-time data transformation.

Answer

Amazon EMR with Spark Streaming

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AWS Lambda function triggered by Kinesis Data Streams

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Kinesis Data Firehose with custom data transformation

Question 8

A company wants to ingest data from multiple SaaS applications into Amazon S3 using a fully managed service that supports schema discovery and transformation. Which AWS service should they use?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3. It supports schema discovery and transformation, making it ideal for ingesting data from multiple SaaS apps without writing code.

Answer

Amazon Kinesis Data Firehose

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AWS Glue

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AWS Data Pipeline

Question 9

A company is building a data lake on Amazon S3 and needs to ingest data from multiple sources. Which of the following AWS services can be used to ingest and transform data in near real-time? (Select TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL (Extract, Transform, Load) service that can ingest data from various sources and transform it in near real-time using its streaming ETL capabilities. Glue can consume data from Amazon Kinesis Data Streams or Apache Kafka, apply transformations using Apache Spark, and write the results to Amazon S3 or other destinations, making it suitable for near real-time data ingestion and transformation.

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Amazon Athena

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AWS Step Functions

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Amazon Simple Queue Service (SQS)

Question 10

A company uses AWS DMS to replicate data from an Amazon RDS for MySQL database to Amazon S3. Which TWO configurations are required to enable continuous change data capture (CDC) from MySQL?

Accepted Answer

Grant REPLICATION CLIENT and REPLICATION SLAVE privileges to the DMS user. Option B is correct because AWS DMS requires the MySQL endpoint user to have REPLICATION CLIENT (to read binlog metadata/status) and REPLICATION SLAVE (to read the binary log events) privileges so it can capture ongoing changes for CDC. Option C is correct because continuous change data capture depends on the MySQL source having binary logging enabled (log_bin=ON) with a suitable binlog_format (e.g., ROW) so DMS can read committed changes from the binlog. Option A is not required, since DMS can replicate to an S3 bucket in a different Region than the source database. Option D is not required, as S3 bucket versioning is unrelated to DMS CDC and is not needed for change capture. Option E is not required, because Multi-AZ is a high-availability deployment choice for the source and does not enable or affect DMS change data capture.

Answer

Ensure the S3 bucket is in the same AWS Region as the source database

Answer

Enable versioning on the target S3 bucket

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Configure the MySQL source to be Multi-AZ

Question 11

A company is using AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError' when the stream has a sudden spike in data volume. Which configuration change would best prevent this error?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the Glue job.. Increasing the number of DPUs for the AWS Glue job provides more compute and memory resources to the job, which directly addresses the MemoryError caused by sudden data volume spikes. Glue DPUs are the unit of processing capacity, and scaling them up allows the job to handle larger volumes without running out of memory. This is the most direct configuration change to prevent memory-related failures.

Answer

Store intermediate results in Amazon RDS.

Answer

Use a batch transformation instead of streaming.

Answer

Increase the number of shards in the Kinesis data stream.

Question 12

A data engineer needs to ingest on-premises CSV files into Amazon S3 every hour. The files are less than 1 GB each. Which service is the most cost-effective and requires the least operational overhead?

Accepted Answer

AWS DataSync. AWS DataSync is the most cost-effective and least overhead option for scheduled, recurring transfers of on-premises CSV files to S3. It provides a simple agent-based setup, supports hourly scheduling, and handles files under 1GB efficiently without complex configuration. In contrast, Amazon Kinesis Data Firehose is designed for streaming data ingestion, not batch file transfers; AWS Snowball Edge is intended for large-scale offline data migrations, not hourly incremental transfers; and AWS Database Migration Service (DMS) is specialized for migrating databases, not file transfers.

Answer

Amazon Kinesis Data Firehose

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AWS Snowball Edge

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AWS Database Migration Service (DMS)

Question 13

An e-commerce company wants to capture clickstream data from its website and store it in Amazon S3 for analytics. The data arrives continuously and the company needs near-real-time processing. Which solution is most appropriate?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the most appropriate solution because it is a fully managed service designed to ingest streaming data and deliver it to destinations like Amazon S3 with near-real-time latency. The company needs continuous clickstream capture and near-real-time processing, which Firehose provides. Option A (AWS Data Pipeline) is for batch processing, not streaming. Option B (AWS Snowball Edge) is for offline data transfer, not real-time. Option D (S3 Transfer Acceleration) improves upload speed but is not a streaming ingestion service.

Answer

AWS Data Pipeline

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AWS Snowball Edge

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Amazon S3 Transfer Acceleration

Question 14

A data engineer is designing a data ingestion pipeline for IoT sensor data. The sensors send JSON messages every second. The data must be available in Amazon S3 within 5 minutes and must be transformed (JSON to Parquet) before storage. Which combination of services meets these requirements?

Accepted Answer

Amazon Kinesis Data Firehose with data transformation and Parquet conversion. Amazon Kinesis Data Firehose natively supports AWS Lambda-based record transformation and can convert the transformed JSON records to Parquet using its built-in format conversion before delivering to S3. Firehose buffers data and delivers within its configurable buffer interval (as low as 60 seconds), comfortably meeting the 5-minute SLA. This is the only option that combines ingestion, transformation, and Parquet conversion in a single managed service.

Answer

Amazon Kinesis Data Streams with AWS Glue streaming ETL

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Amazon Kinesis Data Analytics with output to S3

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Amazon S3 with S3 Event Notifications to AWS Lambda for transformation

Question 15

A company uses AWS Glue crawlers to populate the Data Catalog from data in Amazon S3. The crawler fails to update the schema when new columns are added to the CSV files. What is the most likely cause?

Accepted Answer

The crawler uses a custom classifier that defines a fixed schema.. AWS Glue crawlers use classifiers to infer the schema of data in Amazon S3. If a custom classifier is used and it defines a fixed schema, the crawler will not detect new columns because the classifier's schema is static. The crawler relies on the classifier to parse the data and determine the schema; if the classifier does not include the new columns, they will be ignored. Therefore, the most likely cause is that the crawler uses a custom classifier with a fixed schema.

Answer

The S3 bucket has versioning enabled.

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The crawler is configured to only crawl new partitions.

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The IAM role for the crawler lacks permissions to read the new columns.