DEA-C01 Data Ingestion and Transformation Practice Test 33 — 15 Questions

Question 1

Which TWO options are valid methods to ingest on-premises relational database data into Amazon S3 for analytics? (Choose 2.)

Accepted Answer

AWS Glue ETL job with JDBC connection to source. AWS Glue ETL jobs can connect to on-premises relational databases via JDBC, extract data, and write it directly to Amazon S3 in formats like Parquet or ORC. This is a fully managed, serverless approach suitable for batch ingestion and transformation of structured data for analytics.

Answer

AWS Snowball Edge

Answer

Amazon Kinesis Data Streams with Direct Put

Answer

Amazon AppFlow

Question 2

Which TWO AWS services can be used to transform data in transit during ingestion? (Choose 2.)

Accepted Answer

Amazon Kinesis Data Firehose with Lambda transformation. Amazon Kinesis Data Firehose can invoke an AWS Lambda function to transform streaming data in real time before delivering it to a destination, making it suitable for transforming data in transit during ingestion. AWS Glue ETL can be used for both batch and streaming transformations; specifically, AWS Glue supports streaming ETL jobs that can transform data in transit as it is ingested from sources like Amazon MSK or Kinesis Data Streams. Therefore, both services can transform data during ingestion, albeit with different use cases and latency characteristics.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Athena

Answer

AWS Data Pipeline

Question 3

A company is designing a data ingestion pipeline for real-time IoT sensor data. The data volume peaks at 10,000 messages per second. The pipeline must process messages in order per sensor and persist raw data to Amazon S3 for archival. Which TWO services should be used together to meet these requirements? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams (KDS) provides the ordered, real-time ingestion layer required for per-sensor message ordering, as it partitions data by a partition key (e.g., sensor ID) and guarantees order within a shard. Amazon Kinesis Data Firehose then reliably reads from the KDS stream and delivers the raw data to Amazon S3 for archival, handling buffering, compression, and automatic retries without requiring custom code.

Answer

AWS Lambda

Answer

Amazon AppFlow

Answer

Amazon Simple Queue Service (Amazon SQS)

Question 4

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The Flink application reads from a Kinesis Data Streams source, performs aggregations, and writes results to Amazon S3. The application is experiencing high checkpoint failures, and the processing lag is increasing. The data volume is 50 MB/s with an average record size of 1 KB. Which TWO actions would improve checkpoint reliability and reduce lag? (Choose TWO.)

Accepted Answer

Increase the checkpoint interval in the Flink configuration.. Increasing the checkpoint interval (Option D) reduces the frequency of checkpoint operations, which decreases the overhead on the Flink application and allows it to dedicate more resources to processing data, thereby reducing lag. This is especially effective when checkpoint failures are caused by the system being unable to complete checkpoints within the current interval due to high throughput (50 MB/s).

Answer

Decrease the checkpoint interval to complete checkpoints faster.

Answer

Replace the S3 sink with Kinesis Data Firehose.

Answer

Decrease the parallelism of the Flink application.

Question 5

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The jobs run nightly and take 3 hours to complete. The data volume is growing by 20% each month. The engineer needs to reduce job runtime and cost. The source RDS is a db.r5.large instance. Which approach would be MOST effective?

Accepted Answer

Increase the number of Glue workers and choose a G.1X or G.2X worker type.. Increasing the number of Glue workers and choosing a G.1X or G.2X worker type directly increases parallelism and provides more memory per worker, reducing job runtime at a manageable cost increase. Option A is wrong; reducing DPUs would increase runtime, not reduce it. Option C is wrong because a read replica does not improve Glue processing speed; the bottleneck is Glue's processing capacity, not the source database's read capacity. Option D is wrong because S3 Transfer Acceleration improves upload speed to S3, not Glue job processing.

Answer

Reduce the number of DPUs to lower cost and accept longer runtime.

Answer

Create a read replica of the RDS instance and point the Glue job to the replica.

Answer

Enable S3 Transfer Acceleration on the destination bucket.

Question 6

A company wants to ingest streaming data from thousands of IoT devices into AWS for real-time processing. Each device sends JSON payloads of about 2 KB at a rate of 1 message per second. The data must be processed with a durable, ordered stream per device. Which service should the company use as the ingestion layer?

Accepted Answer

Amazon Kinesis Data Streams.. Amazon Kinesis Data Streams is the correct choice because it provides durable, ordered stream processing per shard, which can be partitioned by device ID to maintain message order for each device. It supports real-time ingestion from thousands of IoT devices at 2 KB per message per second, with a retention period of up to 365 days and the ability to reprocess data via multiple consumers.

Answer

Amazon Simple Queue Service (Amazon SQS) with a FIFO queue.

Answer

Amazon Simple Notification Service (Amazon SNS) with a Lambda subscriber.

Answer

Amazon Kinesis Data Firehose with Direct Put.

Question 7

A company uses Amazon EMR to process large datasets stored in Amazon S3. The data is in Parquet format and partitioned by date. The EMR cluster uses Spark SQL for transformations. Recently, the job has been slow and some tasks are failing due to 'java.lang.OutOfMemoryError'. The cluster has 10 core nodes of type m5.xlarge. Which configuration change would MOST improve performance and stability?

Accepted Answer

Change the core node instance type to r5.xlarge (memory-optimized).. The error 'java.lang.OutOfMemoryError' indicates that the Spark executors are running out of memory during processing. The m5.xlarge instance type provides 16 GiB of memory, but the workload likely requires more memory per task. Switching to r5.xlarge (32 GiB of memory) doubles the available memory per node, reducing memory pressure and preventing task failures, which directly improves stability and performance for memory-intensive transformations.

Answer

Increase the number of Spark partitions using repartition(), but keep the same nodes.

Answer

Increase the number of executor cores in the Spark configuration.

Answer

Enable Kryo serialization in the Spark configuration.

Question 8

A company uses AWS Glue to run ETL jobs that transform data from Amazon S3 (Parquet) into a denormalized format for Amazon Redshift. The Glue job uses the DynamicFrame API. The job is failing with a 'MemoryError' when performing a join operation. The data is skewed on the join key. Which THREE actions can reduce memory usage and improve job stability? (Choose THREE.)

Accepted Answer

Use a broadcast join if one of the tables is small enough.. A broadcast join (using `join` with `broadcast` hint or `DynamicFrame.join(..., transformation_ctx='...')` with broadcast enabled) avoids shuffling the larger table across the cluster by copying the small table to every executor. This eliminates the memory pressure from skewed key distribution during the shuffle phase, reducing the risk of a MemoryError.

Answer

Increase the number of DPUs for the Glue job.

Answer

Repartition the data on the join key before the join operation.

Question 9

A company wants to ingest streaming data from thousands of IoT devices into AWS for real-time analytics. Which AWS service is best suited for this purpose?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is purpose-built for ingesting and processing streaming data at scale from thousands of sources. It can capture and store terabytes of data per hour from IoT devices, enabling real-time analytics with millisecond latencies. The service provides durable, ordered data streams that can be consumed by multiple applications simultaneously.

Answer

Amazon S3

Answer

AWS Lambda

Answer

Amazon RDS

Question 10

A data engineer needs to transform CSV files arriving in an S3 bucket into Parquet format and store them in another S3 bucket. The transformation is simple and on-demand, triggered by data arrival. Which solution is the MOST cost-effective and requires the least operational overhead?

Accepted Answer

Use S3 Events to trigger an AWS Lambda function that transforms the data. Using S3 Events to trigger an AWS Lambda function is the most cost-effective and operationally lightweight solution for simple, on-demand CSV-to-Parquet transformations. Lambda scales automatically with each S3 PUT event, incurs no idle cost, and requires no cluster management, making it ideal for event-driven, low-volume transformations.

Answer

Use Amazon EMR with Spark streaming

Answer

Use Amazon Athena to create a new table with Parquet format

Answer

Use AWS Glue ETL jobs scheduled to run every hour

Question 11

A company uses Kinesis Data Analytics for SQL-based real-time analytics on streaming data. They notice that the application is processing data slower than the incoming rate, causing increased latency. Which action is MOST likely to improve the throughput?

Accepted Answer

Increase the number of Kinesis Processing Units (KPUs) for the application. Kinesis Data Analytics for SQL applications processes data using Kinesis Processing Units (KPUs), which define the compute and memory resources available. When the incoming data rate exceeds the processing capacity, increasing the number of KPUs directly scales the application's parallelism and throughput, allowing it to keep up with the stream. This is the most direct way to reduce latency caused by insufficient processing power.

Answer

Increase the number of shards in the Kinesis data stream

Answer

Enable auto-scaling on the Kinesis data stream

Answer

Decrease the retention period of the Kinesis data stream

Question 12

A data streaming application uses Kinesis Data Streams with 10 shards. The data producer is throttled frequently. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards. Throttling in Kinesis Data Streams occurs when the write throughput exceeds the shard limits. Each shard supports up to 1 MB/s or 1,000 records/s for writes. With 10 shards, the total write capacity is 10 MB/s or 10,000 records/s. Increasing the number of shards (Option D) directly increases the write capacity, resolving the throttling issue by distributing the load across more shards.

Answer

Decrease the data retention period

Answer

Use enhanced fan-out for consumers

Answer

Enable server-side encryption

Question 13

A company needs to transform JSON data from an S3 bucket into a structured format for Amazon Redshift. The transformation should be done serverlessly. Which service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it is a fully managed, serverless ETL service designed specifically for transforming and preparing data for analytics, including converting JSON to structured formats like Parquet or ORC for Amazon Redshift. It can crawl the S3 source, infer schemas, and run Spark-based transformation jobs without provisioning any infrastructure, aligning perfectly with the serverless requirement.

Answer

Amazon EMR

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Amazon Athena

Answer

AWS Lambda

Question 14

A company is ingesting data from multiple sources into S3 using AWS Glue. The data engineer notices that the Glue job is failing with an OutOfMemory error. Which step should be taken to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. AWS Glue jobs run on Apache Spark, which distributes data processing across multiple executors. An OutOfMemory error typically indicates that the data being processed exceeds the memory available to the executors. Increasing the number of DPUs (Data Processing Units) allocates more memory and compute resources to the job, allowing it to handle larger datasets without running out of memory.

Answer

Reduce the volume of incoming data

Answer

Configure the job to use a larger memory setting

Answer

Use a smaller file size for input

Question 15

A company needs to ingest real-time clickstream data from a web application into Amazon Redshift with minimal latency. The data volume is high and requires processing before loading. Which architecture is MOST appropriate?

Accepted Answer

Kinesis Data Streams -> Kinesis Data Firehose -> Redshift. D is correct because Kinesis Data Streams captures high-volume clickstream data in real time, and Kinesis Data Firehose can buffer, transform (e.g., with Lambda), and load the data directly into Amazon Redshift with near-zero latency. This architecture is purpose-built for streaming ingestion with minimal overhead, unlike batch or intermediary storage approaches.

Answer

AWS Glue ETL jobs scheduled every 5 minutes -> Redshift

Answer

S3 -> Lambda -> Redshift

Answer

DynamoDB Streams -> Lambda -> Redshift