DEA-C01 Data Ingestion and Transformation Practice Test 6 — 15 Questions

Question 1

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. The team wants to resolve this issue without increasing costs significantly. Which TWO actions should the team take?

Accepted Answer

Increase the Spark memory overhead parameter in the Glue job configuration.. The correct answers are A and C. Increasing Spark memory overhead per worker (option A) provides more memory for Spark operations. Using the 'g.2x' worker type (option C) offers more memory per worker compared to 'g.1x' without doubling cost. Option B (increasing number of workers) increases cost linearly. Option D (using Python shell) is not suitable for large data. Option E (using DynamicFrame) does not address memory.

Answer

Use DynamicFrame instead of Spark DataFrame for transformations.

Answer

Increase the number of workers to maximum allowed.

Answer

Switch from a Spark job to a Python shell job.

Question 2

A data engineer is ingesting streaming data from an IoT fleet into Amazon S3 using Amazon Kinesis Data Firehose. The data arrives as JSON, but the downstream analytics require Parquet format. Which Firehose transformation should the engineer configure?

Accepted Answer

Configure a Lambda function as a data transformation in Firehose to convert JSON to Parquet.. Option B is correct because Kinesis Data Firehose can convert JSON to Parquet using an AWS Lambda transformation. Option A is wrong because S3 lifecycle policies do not transform data format. Option C is wrong because Kinesis Data Analytics performs real-time analytics, not format conversion. Option D is wrong because S3 batch operations process existing objects, not streaming ingestion.

Answer

Use an S3 lifecycle policy to convert JSON to Parquet.

Answer

Use S3 Batch Operations to convert existing JSON objects to Parquet.

Answer

Use Kinesis Data Analytics to convert the stream to Parquet before writing to S3.

Question 3

A company uses AWS Glue ETL to process data from Amazon S3 and write results to Amazon Redshift. The job fails with a memory error when processing large files. Which action should the data engineer take to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Option C is correct because increasing the number of DPUs provides more memory for processing. Option A is wrong because reducing parallelism may increase memory per worker but typically reduces overall throughput. Option B is wrong because S3 Select filters data server-side but does not reduce memory usage in Glue. Option D is wrong because using a smaller instance type would exacerbate the memory issue.

Answer

Reduce the number of partitions in the Glue job.

Answer

Switch to a smaller instance type in the Glue job configuration.

Answer

Use S3 Select to filter columns before reading into Glue.

Question 4

A data engineer is designing a real-time analytics pipeline for clickstream data. The source is Amazon Kinesis Data Streams, and the data must be stored in Amazon S3 in partitioned Parquet format with near-real-time latency. The engineer must also handle late-arriving data (up to 1 hour). Which combination of services meets these requirements?

Accepted Answer

Use Kinesis Data Firehose with a Lambda transformation to write to S3, and a separate Lambda consumer to reprocess late data from the stream.. Option D is correct because Kinesis Data Firehose can buffer data and deliver to S3 with custom partitioning, and a separate Lambda function can reprocess late data from the stream. Option A is wrong because Kinesis Data Analytics does not partition data by time. Option B is wrong because S3 Batch Operations are for batch, not streaming. Option C is wrong because Glue streaming ETL lacks built-in support for late data handling with Firehose partitioning.

Answer

Use AWS Glue streaming ETL to write to S3 in real-time.

Answer

Use Kinesis Data Analytics with a tumbling window to write to S3.

Answer

Use Kinesis Data Firehose to deliver to S3, and use S3 Batch Operations to process late data.

Question 5

A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3. The data is stored as CSV files. The downstream team requires the data to be in Apache Parquet format. Which change should the data engineer make to the DMS task?

Accepted Answer

Modify the DMS task to use Apache Parquet as the target table preparation mode.. Option C is correct because DMS supports target table preparation mode to convert data to Parquet format. Option A is wrong because S3 lifecycle policies do not convert formats. Option B is wrong because DMS does not support Lambda transformations natively. Option D is wrong because switching to full load only would stop continuous replication.

Answer

Add an S3 lifecycle rule to convert CSV to Parquet.

Answer

Change the DMS task to use full load instead of continuous replication.

Answer

Configure a Lambda function to transform data after DMS writes to S3.

Question 6

A data engineer is building a data ingestion pipeline that reads JSON files from Amazon S3 and loads them into an Amazon Redshift table using COPY commands. The files are gzip compressed and contain nested JSON. The engineer wants to minimize transformation steps. Which approach should the engineer use?

Accepted Answer

Use the COPY command with the 'auto' option to ingest JSON directly.. Option B is correct because Redshift COPY with the 'auto' option can automatically flatten nested JSON. Option A is wrong because Glue ETL is an unnecessary intermediate step. Option C is wrong because Kinesis Data Firehose is for streaming, not batch. Option D is wrong because Athena requires schema definition and is not a direct load to Redshift.

Answer

Use Amazon Athena to query the JSON and INSERT INTO Redshift.

Answer

Use Kinesis Data Firehose to transform and load into Redshift.

Answer

Use AWS Glue ETL to flatten the JSON and write to S3 as CSV, then COPY from CSV.

Question 7

A company is migrating its on-premises data warehouse to Amazon Redshift. The daily batch load from the source database takes 6 hours using a single-node Redshift cluster. The engineer needs to reduce load time to under 2 hours without increasing cost significantly. Which strategy should the engineer adopt?

Accepted Answer

Increase the number of nodes in the Redshift cluster and use parallel COPY from multiple files.. Option A is correct because increasing the node count provides more parallelism for the COPY command, reducing load time. Option B is wrong because compression reduces storage, not load time. Option C is wrong because distributing data across nodes helps queries, not loads. Option D is wrong because VPC routing changes do not affect performance.

Answer

Use COPY with compression (gzip) to reduce data volume.

Answer

Use a VPC endpoint to improve network throughput to S3.

Answer

Change the table distribution style to EVEN to distribute data evenly.

Question 8

A data engineer needs to ingest data from an external HTTP API into Amazon S3. The API returns JSON data for a list of users, updated hourly. The engineer wants to use a serverless solution with minimal operational overhead. Which AWS service should the engineer use?

Accepted Answer

Amazon AppFlow with an HTTP connector on a scheduled flow.. Option D is correct because Amazon AppFlow can connect to HTTP APIs and load data to S3 on a schedule. Option A is wrong because Kinesis Data Firehose requires a stream source. Option B is wrong because Glue ETL can do it but requires more configuration. Option C is wrong because Lambda alone lacks scheduling.

Answer

Amazon Kinesis Data Firehose with a custom HTTP endpoint.

Answer

AWS Lambda function triggered by CloudWatch Events.

Answer

AWS Glue ETL job triggered by EventBridge.

Question 9

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that is failing to deliver data to an Amazon S3 bucket. The stream is configured with a Lambda transformation function. The CloudWatch logs show that the Lambda function is timing out. Which action should the engineer take to resolve the issue?

Accepted Answer

Increase the Lambda function timeout setting.. Option C is correct because increasing the Lambda timeout allows the function to complete processing. Option A is wrong because the issue is timeout, not memory. Option B is wrong because the stream configuration does not control Lambda timeout. Option D is wrong because reducing the buffer interval may increase invocation frequency but not fix the timeout.

Answer

Reduce the Firehose buffer interval.

Answer

Decrease the Lambda function's batch size in Firehose.

Answer

Increase the memory allocated to the Lambda function.

Question 10

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for MySQL and load it into Amazon S3. The job runs daily and processes incremental changes using the JDBC connection. Recently, the job has been failing with a 'Communications link failure' error. The RDS instance is in a private subnet. Which step should the engineer take first to diagnose the issue?

Accepted Answer

Check the security group and network ACL rules for the RDS instance and the Glue connection.. Option B is correct because network connectivity between Glue and RDS is likely the issue, and checking security groups and subnets is the first step. Option A is wrong because the error is not about authentication. Option C is wrong because S3 permissions are not related to the link failure. Option D is wrong because the job type (Spark vs Python) does not affect connectivity.

Answer

Verify that the IAM role used by Glue has the correct permissions to access RDS.

Answer

Change the Glue job type from Spark to Python shell.

Answer

Check that the JDBC driver is compatible with the Glue version.

Question 11

A data engineer is designing a data ingestion pipeline for streaming social media data. The data must be ingested with low latency (seconds) and stored in Amazon S3 for long-term analytics. The engineer also needs to perform real-time aggregations. Which TWO services should the engineer use? (Choose two.)

Accepted Answer

Amazon Kinesis Data Analytics. Option A and D are correct. Kinesis Data Streams provides low-latency ingestion, and Kinesis Data Analytics performs real-time aggregations. Option B is wrong because Kinesis Data Firehose has higher latency (minutes). Option C is wrong because S3 is storage, not stream processing. Option E is wrong because Glue ETL is batch-oriented.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue ETL

Answer

Amazon S3

Question 12

A company is building a data lake on Amazon S3. The data sources include relational databases, streaming data, and log files. The data engineer needs to ensure that the data ingestion pipeline can handle schema evolution, support both batch and streaming, and provide a unified metadata catalog. Which THREE services should the engineer use? (Choose three.)

Accepted Answer

AWS Glue. Options A, C, and D are correct. AWS Glue provides a metadata catalog and ETL for batch. Kinesis Data Firehose handles streaming ingestion to S3. S3 stores the data. Option B is wrong because Athena is a query service, not ingestion. Option E is wrong because DynamoDB is not used for data lake ingestion.

Answer

Amazon DynamoDB

Answer

Amazon Athena

Question 13

A data engineer is troubleshooting a slow-running AWS Glue ETL job that reads from Amazon S3 and writes to Amazon Redshift. The job processes 500 GB of CSV data daily. The engineer wants to improve performance. Which THREE actions should the engineer take? (Choose three.)

Accepted Answer

Use a JDBC connection with a higher batch size for writing to Redshift.. Options A, C, and D are correct. Increasing the number of DPUs provides more parallelism. Partitioning the input data improves read performance. Using a JDBC connection with appropriate batch size improves write performance. Option B is wrong because reducing the number of files may reduce parallelism. Option E is wrong because switching to a single node cluster reduces parallelism.

Answer

Switch to a single-node Redshift cluster to reduce latency.

Answer

Reduce the number of input files by combining them into larger files.

Question 14

Refer to the exhibit. A data engineer is configuring an IAM policy for an AWS Glue ETL job that reads data from the 'my-data-bucket' S3 bucket, transforms it, and writes the output back to the same bucket. The engineer wants to prevent accidental deletion of objects. Based on the policy, which statement is true about the Glue job's permissions?

Accepted Answer

The job can read and write objects, but cannot delete objects.. Option D is correct. The policy allows s3:GetObject and s3:PutObject, and explicitly denies s3:DeleteObject. Option A is wrong because the job can read and write. Option B is wrong because the Deny effect overrides any Allow. Option C is wrong because the job can read.

Answer

The job can write objects but cannot read objects.

Answer

The job can read objects but cannot write objects.

Answer

The job can read and write, but may also delete objects.

Question 15

Refer to the exhibit. A data engineer has configured an S3 event notification to send an event to an SQS queue when objects are created in the 'incoming/' prefix. The engineer wants to trigger an AWS Lambda function to process the object. However, the Lambda function is not being invoked. What is the most likely cause?

Accepted Answer

Lambda is not configured as an event source for the SQS queue.. Option B is correct. The event notification sends to SQS, not directly to Lambda. To invoke Lambda, the queue must be configured as an event source for Lambda. Option A is wrong because the prefix is correct. Option C is wrong because permissions are needed but not the primary cause; the Lambda function is not triggered by SQS events unless Lambda polls the queue. Option D is wrong because the queue exists and is configured.

Answer

The Lambda function lacks permission to read from the S3 bucket.

Answer

The SQS queue does not exist or is in a different account.

Answer

The S3 event notification filter prefix is incorrect.