DEA-C01 Data Ingestion and Transformation Practice Test 26 — 15 Questions

Question 1

A company has a 100 TB dataset stored on-premises in a Hadoop cluster. They want to ingest this data into Amazon S3 for processing with AWS Glue. The company has a limited time window and a slow internet connection. Which strategy is MOST appropriate?

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

Accepted Answer

Use AWS Snowball Edge to physically ship the data to AWS.. AWS Snowball Edge is the most appropriate strategy because the dataset is 100 TB, the time window is limited, and the internet connection is slow. Snowball Edge provides a physical storage device that can be shipped to AWS, bypassing network bandwidth constraints entirely. This approach is designed for large-scale data transfers (typically over 10 TB) where network transfer would be impractical or exceed the available time window.

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Use AWS DataSync over the existing internet connection.

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Use Amazon S3 Transfer Acceleration to speed up the upload.

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Use AWS Direct Connect to establish a high-bandwidth connection.

Question 2

A data engineer needs to ingest streaming data from a social media API into Amazon S3 for batch analytics. The data arrives at a rate of 500 records per second. Which service should be used to capture the stream?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time streaming data ingestion at scale, supporting throughput of up to 1 MB/s or 1,000 records per second per shard. With 500 records per second, Kinesis can reliably capture and store the social media API data for up to 365 days, enabling batch analytics via S3 delivery through Kinesis Firehose or custom consumers.

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Amazon Simple Notification Service (SNS)

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Amazon Simple Queue Service (SQS)

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Amazon MQ

Question 3

A company is designing a data ingestion pipeline for real-time sensor data from thousands of devices. The data must be processed with low latency and stored in Amazon S3. Which TWO services would be appropriate for this use case? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to ingest real-time streaming data, transform it on the fly (e.g., convert to Parquet/ORC), and deliver it directly to Amazon S3 with low latency. It handles buffering, compression, and partitioning automatically, making it ideal for the described sensor data pipeline.

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AWS Glue

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AWS DataSync

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Amazon Athena

Question 4

A company uses AWS Glue to transform data from Amazon S3 into Parquet format. The job fails with an out-of-memory error for large files. Which TWO actions can resolve this issue? (Choose TWO.)

Accepted Answer

Increase the number of DPUs allocated to the job.. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more memory and compute capacity, which directly addresses out-of-memory errors when processing large files. AWS Glue uses Apache Spark under the hood, and each DPU provides 4 vCPU and 16 GB of memory, so adding DPUs scales the available resources for in-memory transformations.

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Change the input format from CSV to JSON.

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Use the Glue streaming ETL feature.

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Enable CloudWatch logs for detailed error analysis.

Question 5

A company is building a data lake on Amazon S3 and needs to ingest data from multiple sources. The ingestion must be automated and handle schema changes. Which THREE services can be used together to achieve this? (Choose THREE.)

Accepted Answer

AWS Glue Crawler. AWS Glue Crawler (B) automatically discovers and catalogs schemas from data sources. Amazon Kinesis Data Firehose (C) ingests streaming data into S3. AWS Lambda (E) can transform data on the fly. Together, they automate ingestion and handle schema changes. Amazon Redshift (A) is a data warehouse, not a data lake ingestion service. Amazon EMR (D) is a big data processing framework, not primarily for automated schema change handling.

Answer

Amazon Redshift

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Amazon EMR

Question 6

An IAM policy is attached to an AWS Glue job. The job needs to read from and write to S3 buckets, and also trigger other Glue jobs. The job is failing with an AccessDenied error when trying to write to a bucket named 'example-bucket'. What is the MOST likely cause?

Accepted Answer

The bucket name in the resource ARN does not match the actual bucket name.. The AccessDenied error when writing to 'example-bucket' most likely occurs because the resource ARN in the IAM policy specifies a different bucket name, causing the S3 service to deny the s3:PutObject action. IAM policies require exact ARN matches for resource-based permissions, and a mismatch between the ARN bucket name and the actual bucket name will result in an implicit deny, even if the action is allowed.

Answer

The policy does not include s3:PutObject action.

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The policy uses a resource ARN with a wildcard, which is not allowed.

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The policy does not allow Glue actions.

Question 7

A data engineer runs the describe-stream command and sees the output above. The stream has a retention period of 24 hours. The engineer needs to ensure that consumers can replay data for up to 7 days. Which action is required?

Accepted Answer

Use the IncreaseStreamRetentionPeriod API to set retention to 168 hours.. The describe-stream output shows a retention period of 24 hours, but the requirement is to allow consumers to replay data for up to 7 days (168 hours). Amazon Kinesis Data Streams supports modifying the retention period dynamically without recreating the stream, using the IncreaseStreamRetentionPeriod API or the update-shard-count command. Option C correctly uses this API to set retention to 168 hours, which is the maximum supported retention period for Kinesis Data Streams.

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Increase the number of shards to allow more data storage.

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Delete the stream and recreate it with a longer retention period.

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Create new consumer applications that read from the stream.

Question 8

An IAM policy includes the above resource ARN for CloudWatch Logs. A data engineer needs to allow a Lambda function to create log streams and put logs to the log group 'my-log-group'. However, the Lambda function is failing with access denied. What is the issue?

Accepted Answer

The ARN does not include the log-stream portion.. The ARN provided in the policy is for the log group itself (arn:aws:logs:region:account-id:log-group:my-log-group), but CloudWatch Logs requires separate permissions for the log-stream subresource. To allow a Lambda function to create log streams and put logs, the ARN must include the log-stream portion, typically with a wildcard like arn:aws:logs:region:account-id:log-group:my-log-group:log-stream:*. Without this, the IAM policy does not grant access to the log-stream actions (CreateLogStream, PutLogEvents), causing an access denied error even if the log group ARN is correct.

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The region in the ARN does not match the Lambda function's region.

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The Lambda function does not have an execution role.

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The ARN is incorrectly formatted because of the wildcard.

Question 9

A company wants to ingest streaming data from thousands of IoT devices into Amazon S3 with minimal latency and then transform the data using Spark SQL. Which AWS service should be used for data ingestion?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed for ingesting streaming data into Amazon S3 with near-real-time latency (typically 60 seconds or less). It can directly write data to S3 without requiring custom code or additional infrastructure, and it supports optional transformations via AWS Lambda, making it ideal for the described use case of streaming IoT data ingestion.

Answer

Amazon EMR

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AWS Glue

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Amazon Athena

Question 10

A data engineering team is troubleshooting a slow AWS Glue ETL job that reads from an Amazon DynamoDB table and writes to Amazon S3 in Parquet format. The job processes 50 GB of data. Which action would most effectively improve job performance?

Accepted Answer

Increase the number of DPUs. Increasing the number of DPUs (Data Processing Units) for the AWS Glue job directly allocates more distributed computing resources (CPU, memory, and network bandwidth) to parallelize the read from DynamoDB and the write to S3. Since the job processes 50 GB of data, the bottleneck is likely the throughput of the Glue Spark cluster, and adding DPUs increases parallelism, reducing overall execution time.

Answer

Use S3 Select to push down filters

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Reduce the batch size in the DynamoDB connector

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Change output to JSON format to reduce overhead

Question 11

A company needs to ingest data from an on-premises Oracle database into Amazon Redshift for analytics. The data volume is 500 GB and the network bandwidth is limited. Which AWS service should be used for the initial one-time data migration?

Accepted Answer

AWS Snowball. AWS Snowball is the correct choice for the initial one-time migration of 500 GB of data from an on-premises Oracle database to Amazon Redshift when network bandwidth is limited. Snowball provides a physical storage device that bypasses network constraints entirely, allowing you to transfer large volumes of data securely and quickly by shipping the device to AWS for ingestion into Amazon S3, which can then be loaded into Redshift. This approach avoids the prolonged transfer times and potential bottlenecks associated with limited bandwidth, making it ideal for a one-time, large-scale data migration.

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AWS Direct Connect

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Amazon S3 Transfer Acceleration

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AWS Database Migration Service (DMS)

Question 12

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

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Add an Amazon SQS queue between Lambda and DynamoDB

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Increase the Lambda function memory

Question 13

A data pipeline uses AWS Glue to read from an Amazon S3 bucket containing millions of small CSV files (each < 1 MB). The ETL job is slow. Which optimization would most improve performance?

Accepted Answer

Use S3 file grouping to combine small files. Using Amazon S3 file grouping or converting to columnar format like Parquet reduces the number of files and improves read performance. Increasing workers helps, but file consolidation is more impactful. Using G.1X worker type may help, but grouping files is key. Using Spark SQL directly does not address the small files problem.

Answer

Write the ETL script using PySpark instead of Scala

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Increase the number of Glue workers

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Use the G.1X worker type for more memory

Question 14

A company wants to transform data in Amazon S3 using SQL queries without provisioning servers. The transformations are ad-hoc and run occasionally. Which service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it enables serverless, ad-hoc SQL querying directly on data stored in Amazon S3 without requiring any infrastructure provisioning. Since the transformations are occasional and ad-hoc, Athena's pay-per-query model and zero setup overhead align perfectly with the requirement.

Answer

AWS Glue

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Amazon Redshift Spectrum

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Amazon EMR

Question 15

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

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The data size exceeds the 1 MB limit per record

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