DEA-C01 Data Ingestion and Transformation Practice Test 44 — 15 Questions

Question 1

A company wants to ingest real-time clickstream data from a website into Amazon S3 with minimal code. The data should be delivered within 60 seconds of generation. Which AWS service should be used?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is a fully managed service that can ingest real-time streaming data and deliver it to Amazon S3 with minimal code. It can buffer data and deliver within 60 seconds, making it ideal for this use case. Firehose requires no custom code for delivery and can scale automatically.

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Kinesis Data Streams

Answer

Amazon S3 Transfer Acceleration

Question 2

A data engineer is designing a data ingestion pipeline to load data from an on-premises Oracle database into Amazon Redshift. The pipeline must capture changes (inserts, updates, deletes) with low latency and minimal impact on the source database. Which combination of AWS services should the engineer use?

Accepted Answer

AWS Database Migration Service (DMS) to Amazon S3, then COPY into Redshift. AWS DMS with ongoing replication (change data capture) can capture changes from Oracle with minimal impact and replicate to S3, then COPY into Redshift. Option B is wrong because Kinesis Data Streams requires a custom producer to capture Oracle changes and does not provide native CDC from Oracle databases. Option C is wrong because AWS Glue with JDBC is batch-oriented and does not support real-time change data capture natively. Option D is wrong because AWS Lambda can process events but is not designed for continuous, low-latency CDC from a database and would require polling or triggers.

Answer

Amazon Kinesis Data Streams with a custom producer on Oracle

Answer

AWS Glue with JDBC connection to Oracle, writing to Redshift

Answer

AWS Lambda reading from Oracle logs and writing to Redshift

Question 3

A company is using Amazon Kinesis Data Firehose to ingest data into Amazon S3. The data must be transformed from JSON to Parquet format before delivery. Which feature should be enabled on the Firehose delivery stream?

Accepted Answer

Format conversion (Parquet/ORC). Amazon Kinesis Data Firehose has a built-in format conversion feature that can automatically convert input data from JSON to Parquet or ORC format before delivery to Amazon S3. Option A (Amazon Kinesis Data Analytics) is for real-time stream processing, not format conversion within Firehose. Option B (Amazon S3 event notifications) triggers notifications on S3 events, not data transformation. Option D (AWS Lambda transformation) allows custom code for data transformation but is not specifically for converting JSON to Parquet; the built-in format conversion is the appropriate feature for this task.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3 event notifications

Answer

AWS Lambda transformation

Question 4

A data engineer is designing a streaming ingestion pipeline using Amazon Kinesis Data Streams. The stream has 10 shards, and the data volume is expected to grow by 50% over the next month. The engineer needs to ensure that the pipeline can scale without manual intervention. Which approach should be used?

Accepted Answer

Switch the Kinesis stream to on-demand capacity mode. Kinesis Data Streams on-demand capacity mode automatically scales shard capacity up or down based on observed throughput, eliminating the need to manually provision or add shards. It handles the 50% growth without any CloudWatch alarms, Lambda functions, or Auto Scaling groups. This is the only option that provides fully managed, hands-off scaling.

Answer

Set up a CloudWatch Alarm to trigger a Lambda function to add shards

Answer

Use an Auto Scaling group to add more shards

Answer

Configure the stream to use a Lambda function that scales shards

Question 5

A company needs to ingest data from multiple SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data sources have different schemas and update frequencies. Which AWS service should be used to build this ingestion pipeline with minimal code?

Accepted Answer

Amazon AppFlow. Amazon AppFlow (Option D) is the correct answer because it is purpose-built for ingesting data from SaaS applications like Salesforce and Marketo into Amazon S3 with minimal code. It supports various source connectors, handles schema variations, and allows scheduling based on update frequencies. AWS Data Pipeline (A) requires more manual configuration and code for connectors. AWS Glue (B) has connectors but is more complex for this use case, often requiring additional ETL scripting. Amazon Kinesis Data Firehose (C) is designed for streaming data, not batch extraction from SaaS APIs.

Answer

AWS Data Pipeline

Answer

AWS Glue

Answer

Amazon Kinesis Data Firehose

Question 6

A data engineer is using AWS Glue to run an ETL job that reads data from Amazon DynamoDB and writes to Amazon Redshift. The job fails with a 'ThroughputExceededException' error. What is the most likely cause?

Accepted Answer

The DynamoDB table's read capacity is insufficient for the Glue job's read rate. A ThroughputExceededException from DynamoDB indicates that the read or write request rate exceeded the provisioned throughput (RCUs/WCUs) or the burst capacity of the table or index. When AWS Glue reads from DynamoDB, it consumes read capacity units; if the table's read capacity is too low for the parallel read rate of the Glue job, DynamoDB throttles the requests and the job fails.

Answer

The Glue job has a timeout setting that is too low

Answer

The Redshift cluster's concurrency scaling is insufficient

Answer

The S3 bucket where Glue writes temporary data does not have proper permissions

Question 7

A company uses Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The data delivery is delayed by up to 5 minutes. The engineer wants to reduce the delay to under 1 minute. Which parameter should be adjusted?

Accepted Answer

Decrease the buffer interval in Kinesis Data Firehose.. Decreasing the buffer interval reduces the time Kinesis Data Firehose waits before delivering a batch, thus lowering latency to under 1 minute. Option A is incorrect because error logging to CloudWatch does not affect delivery timing. Option B is incorrect because increasing the buffer size would actually increase the delay as Firehose waits for more data to accumulate. Option C is incorrect because enabling data compression reduces storage size but has no impact on delivery frequency.

Answer

Enable error logging to CloudWatch.

Answer

Increase the buffer size in Kinesis Data Firehose.

Answer

Enable data compression.

Question 8

A company has a large volume of CSV files in S3 that need to be transformed into Parquet using AWS Glue. The files are partitioned by date. The engineer wants to minimize costs by processing only new files each day. Which approach should be used?

Accepted Answer

Enable job bookmarks in the Glue job.. AWS Glue job bookmarks track the state of data processed by a job, so subsequent runs only process new or changed data. When enabled, bookmarks persist the last processed path or partition, allowing the daily job to skip already-transformed files and reduce runtime and cost. This directly satisfies the requirement to process only new files each day.

Answer

Use S3 partition discovery to automatically read new partitions.

Answer

Schedule the Glue job to run daily and process all files.

Answer

Use S3 Event Notifications to trigger the Glue job on each new file.

Question 9

A data engineer is designing a data ingestion pipeline for real-time clickstream data. Which TWO services can be used to ingest the data into Amazon Kinesis Data Streams?

Accepted Answer

Kinesis Producer Library (KPL). Options B and D are correct. The Kinesis Producer Library (KPL) is a library for producers to send data to Kinesis Data Streams. AWS SDK can also be used directly. Option A is wrong because Amazon S3 is a storage service, not a producer. Option C is wrong because Kinesis Data Firehose is a downstream consumer or delivery service, not a producer. Option E is wrong because AWS Glue is an ETL service, not a producer.

Answer

Amazon S3

Answer

Kinesis Data Firehose

Answer

AWS Glue

Question 10

A company uses AWS Glue to transform data in S3. The Glue job fails with memory errors. Which THREE actions can help resolve this?

Accepted Answer

Optimize the transformation to use pushdown predicates.. Options A, B, and C are correct. Using pushdown predicates (A) reduces the amount of data read by filtering at the data source, which can alleviate memory pressure. Using a larger worker type (B), such as G.2X, increases the memory available per worker, directly addressing out-of-memory errors. Increasing the number of DPUs (C) adds more workers, distributing the memory load. Option D (increasing job timeout) does not solve memory issues, and Option E (decreasing DPUs) would reduce available memory, making the problem worse.

Answer

Increase the job timeout.

Answer

Decrease the number of DPUs.

Question 11

A data engineer is implementing a CDC (Change Data Capture) pipeline from a relational database to Amazon S3 using AWS Database Migration Service (DMS). Which TWO configurations are required for continuous replication?

Accepted Answer

Enable binary logging on the source database.. Option B is correct because AWS DMS continuous replication (CDC) requires the source relational database to expose its change stream — for MySQL/MariaDB that means binary logging (log_bin) enabled with binlog_format=ROW, and for other engines the equivalent (e.g., Oracle supplemental logging, SQL Server MS-CDC). Option D is correct because the DMS task's migration type must be set to 'Full load and ongoing replication' (or 'Ongoing replication' if data is already loaded) so DMS performs the initial load and then continuously applies CDC changes to Amazon S3. Option A is not required — transformation rules are optional and only used to rename, filter, or modify schema/data during migration. Option C is not required — a VPC endpoint is only needed for private connectivity to services like S3 when using a VPC-attached replication instance, not as a general CDC requirement. Option E is not required — DMS writes to S3 as CSV or Parquet files and does not need a pre-created target table.

Answer

Define transformation rules in the DMS task.

Answer

Configure a VPC endpoint for DMS.

Answer

Pre-create the target table in S3.

Question 12

A company wants to ingest real-time streaming data from thousands of IoT devices into AWS for immediate processing. Which service is designed for ingesting large volumes of streaming data with low latency?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is purpose-built for real-time streaming data ingestion at scale. Option A (S3) is object storage, not designed for streaming ingestion. Option B (DMS) is for database migration, not real-time data streaming. Option C (SQS) is a message queue service that is pull-based and not optimized for high-throughput streaming with low latency. Only option D (Kinesis Data Streams) meets all requirements for ingesting large volumes of streaming data with low latency.

Answer

Amazon Simple Storage Service (S3)

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Simple Queue Service (SQS)

Question 13

A company ingests JSON data from an S3 bucket into a Glue ETL job. The data contains nested structures and arrays. The team wants to flatten the data into a tabular format for analysis in Athena. Which Glue transformation is appropriate?

Accepted Answer

Relationalize. The Relationalize transformation is specifically designed to flatten nested JSON and arrays into a tabular format suitable for Athena. Option A (Map) applies a function to each record but does not flatten structures. Option C (Filter) selects rows based on a condition. Option D (DropNullFields) removes null fields but does not address nested structures.

Answer

Map

Answer

Filter

Answer

DropNullFields

Question 14

A data pipeline uses AWS Glue to process data from an S3 data lake. The pipeline fails intermittently with a 'ThrottlingException' when writing to a DynamoDB table. What is the MOST likely cause?

Accepted Answer

The DynamoDB table's write capacity is insufficient for the workload.. A ThrottlingException from DynamoDB indicates that the request rate to the table has exceeded the provisioned write capacity. AWS Glue jobs can generate high-throughput writes, and if the DynamoDB table's write capacity units (WCUs) are not sufficient to handle the burst, DynamoDB will throttle the requests. This is the most direct cause of the intermittent failure described.

Answer

The network connection between Glue and DynamoDB is unstable.

Answer

The Glue job's timeout setting is too low.

Answer

The Glue job does not have sufficient IAM permissions to write to DynamoDB.

Question 15

A company needs to ingest data from an on-premises database to Amazon S3 with minimal impact on the source database. The data volume is several TB. Which AWS service is best suited for this task?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is best suited because it can continuously replicate data from an on-premises database to Amazon S3 with minimal impact on the source. DMS uses change data capture (CDC) to capture only incremental changes after an initial full load, avoiding heavy read loads on the source database. This makes it ideal for migrating several TB of data while keeping the source operational.

Answer

AWS Direct Connect

Answer

AWS Snowball Edge

Answer

Amazon S3 Transfer Acceleration