DEA-C01 Practice Exam 56 — 20 Questions

Question 1

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data must be transformed and stored in Amazon S3 for batch analytics. The engineer wants to use AWS Lambda for transformation. Which TWO configurations are required? (Choose two.)

Accepted Answer

Create an event source mapping from the Kinesis stream to the Lambda function.. Options A and D are correct. Option A: Lambda needs an event source mapping to poll from Kinesis. Option D: Lambda needs an IAM role with permissions to read from Kinesis and write to S3. Option B is wrong because Lambda does not write directly to S3 via Kinesis; it writes via the Lambda function. Option C is wrong because Lambda does not use SQS to trigger from Kinesis. Option E is wrong because Lambda does not write to S3 via Firehose; Firehose can directly write to S3.

Answer

Configure the Lambda function to write to S3 via Kinesis Data Firehose.

Answer

Configure the Kinesis stream to send records directly to S3.

Answer

Set up an SQS queue as a destination for Lambda errors.

Question 2

A company ingests streaming data from multiple sources into a single Kinesis Data Streams stream. Each source produces records with a different schema. The data must be routed to different S3 prefixes based on the source. Which approach minimizes transformation overhead?

Accepted Answer

Use a single Kinesis Data Firehose with a Lambda transformation that reads schema metadata from DynamoDB to determine the S3 prefix.. Option A is correct because using a DynamoDB table to store schema metadata and a Lambda function to route records to the appropriate Firehose delivery stream is efficient and scalable. Option B is wrong because it requires multiple Kinesis streams, increasing cost and complexity. Option C is wrong because ingesting into S3 and then using Glue to route adds latency. Option D is wrong because using Kinesis Data Analytics for routing is overkill and not designed for this purpose.

Answer

Ingest all data into S3 and use AWS Glue ETL jobs to partition and route data to different prefixes.

Answer

Use separate Kinesis Data Streams for each source and configure separate Firehose delivery streams.

Answer

Use Kinesis Data Analytics to run SQL queries that route data to different Firehose streams.

Question 3

A company stores IoT sensor data in S3 as JSON files. They need to convert the data to Parquet format for efficient querying with Amazon Athena. Which AWS service can perform this transformation with minimal effort?

Accepted Answer

AWS Glue ETL job. Option B is correct because AWS Glue ETL can easily convert JSON to Parquet. Option A is wrong because Athena is a query engine, not a transformation service. Option C is wrong because Lambda is for small, event-driven transformations. Option D is wrong because Kinesis Data Firehose is for streaming data.

Answer

Kinesis Data Firehose

Answer

Amazon Athena

Answer

AWS Lambda

Question 4

A company has CSV files in an S3 bucket that need to be converted to Parquet and loaded into a Redshift table daily. The transformation is a simple schema mapping without joins. Which AWS Glue feature is BEST suited for this task?

Accepted Answer

AWS Glue ETL job. Option B is correct because Glue ETL job is the standard way to transform and load data. Option A (Crawler) only catalogs data. Option C (DataBrew) is a visual tool, but the question asks for Glue feature for automated daily jobs. Option D (Workflows) orchestrates jobs but does not perform transformation.

Answer

AWS Glue DataBrew

Answer

AWS Glue Workflow

Answer

AWS Glue Crawler

Question 5

Refer to the exhibit. A data engineer runs a Glue job manually and receives a ThrottlingException. The engineer checks the job run history and sees a previous failure with the same error. What is the MOST likely cause of the throttling, and which solution is MOST appropriate?

Accepted Answer

Implement retry logic with exponential backoff in the script that calls start-job-run.. Option B is correct because the error 'Rate exceeded' indicates the Glue API rate limit has been reached. The most likely cause is multiple concurrent job runs or too many API calls. Implementing retry logic with exponential backoff in the job or script that triggers the job will handle transient throttling. Option A (increasing DPUs) does not affect API rate. Option C (deleting old job runs) might free up quota but is not the best practice. Option D (using reserved capacity) is not applicable for Glue API calls.

Answer

Increase the number of DPUs for the job to reduce runtime.

Answer

Use AWS Glue reserved capacity to guarantee API throughput.

Answer

Delete old job runs to reduce the number of entries in the job run history.

Question 6

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application reads from a Kinesis stream and writes aggregated results to an Amazon S3 bucket. The company notices that the application is falling behind and the checkpoint duration is increasing. Which THREE actions should the data engineer take to improve performance? (Choose THREE.)

Accepted Answer

Use multiple S3 prefixes in the output path to avoid throttling.. Options A, C, and E are correct. Increasing parallelism allows more parallel processing. Using S3 with multiple prefixes reduces S3 write throttling. Increasing checkpoint interval reduces overhead. Option B is wrong because heap memory increase is not directly related to checkpoint duration. Option D is wrong because reducing the number of shards would decrease throughput.

Answer

Decrease the number of shards in the source Kinesis stream.

Answer

Increase the heap memory of the Flink application.

Question 7

A data engineer is using AWS Glue ETL to transform data from an S3 data lake. The job fails with a memory error. Which approach should be used to resolve this issue without major code changes?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Option C is correct because increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more memory and parallelism. Option A is wrong because using a smaller file format may not address memory issues. Option B is wrong because rewriting in PySpark is a major code change. Option D is wrong because using a different service is unnecessary.

Answer

Rewrite the ETL script in PySpark instead of Scala

Answer

Change the input file format from CSV to Parquet

Answer

Use Amazon EMR instead of AWS Glue

Question 8

A company is designing a data ingestion pipeline for clickstream data from a website. The data must be ingested in near real-time. Which TWO services can be used together to build this pipeline?

Accepted Answer

Amazon Kinesis Data Streams. Option A and Option D are correct because Kinesis Data Streams can ingest the clickstream data, and Kinesis Data Firehose can deliver it to S3. Option B is wrong because S3 is not a real-time ingestion service. Option C is wrong because DynamoDB is a database, not a streaming ingestion service. Option E is wrong because SQS is for message queuing, not streaming ingestion.

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon S3

Answer

Amazon DynamoDB

Question 9

A data engineer is tasked with ingesting on-premises database snapshots (full load) into Amazon S3 on a daily basis. The database is PostgreSQL and the snapshot size is 50 GB. The network link is 1 Gbps. Which approach is the MOST time-efficient and cost-effective?

Accepted Answer

Use AWS Database Migration Service (DMS) with S3 as target.. Option B is correct because AWS DMS can perform a full load from on-premises PostgreSQL to S3 efficiently, and it's a managed service. Option A (CLI) would be manual and slow. Option C (Snowball) is for larger datasets or slow networks; here 50 GB at 1 Gbps takes ~7 minutes, so Snowball is overkill. Option D (Lambda + pg_dump) adds custom code and complexity.

Answer

Use AWS Snowball Edge to transfer the snapshot.

Answer

Use AWS CLI to copy the snapshot file directly to S3.

Answer

Write a Lambda function to run pg_dump and upload to S3.

Question 10

A data engineer is designing a data ingestion pipeline for real-time clickstream data from a website. The data must be stored in Amazon S3 in near-real time, and also be available for real-time analytics using Amazon Athena. The pipeline must handle occasional spikes of up to 10x the normal throughput. Which combination of services should the engineer use?

Accepted Answer

Amazon Kinesis Data Streams with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.. Option A is correct because Kinesis Data Streams can handle high throughput spikes, Lambda can process and write to S3, and Athena can query directly. Option B is wrong because SQS is pull-based and does not support push to S3 directly; Lambda would need to poll, adding latency. Option C is wrong because DMS is for database migration, not real-time clickstream. Option D is wrong because Glue is batch-oriented and not suitable for low-latency streaming.

Answer

Amazon Simple Queue Service (SQS) with AWS Lambda to write to Amazon S3, and Amazon Athena for queries.

Answer

AWS Database Migration Service (DMS) to stream data to Amazon S3, and Amazon Athena for queries.

Answer

Amazon Kinesis Data Firehose with AWS Glue for transformation, and Amazon Athena for queries.

Question 11

Refer to the exhibit. A data engineer is setting up an Amazon Kinesis Data Firehose delivery stream that writes to an S3 bucket named 'example-bucket'. The IAM role assumed by Firehose has the attached policy shown. When testing, the Firehose delivery stream fails with an access denied error. What is the most likely cause?

Accepted Answer

The IAM policy is missing the s3:AbortMultipartUpload and s3:ListBucket actions.. Option A is correct because Kinesis Data Firehose requires the s3:AbortMultipartUpload and s3:ListBucket permissions to write to S3. The policy only allows s3:PutObject and s3:GetObject. Option B is wrong because SSE is not required. Option C is wrong because the bucket policy is not shown to be restrictive. Option D is wrong because encryption keys are not mentioned.

Answer

The S3 bucket has server-side encryption enabled that needs additional permissions.

Answer

The IAM role does not have permission to use AWS KMS keys.

Answer

The bucket policy denies access from the Firehose service principal.

Question 12

A company is migrating a legacy on-premises ETL pipeline to AWS. The pipeline processes daily batch files from an FTP server. The data must be transformed using complex business logic before being loaded into Amazon Redshift. Which THREE AWS services should be used for this migration?

Accepted Answer

Amazon Redshift. Option A, Option C, and Option D are correct because AWS Transfer Family can ingest files from FTP, AWS Glue can perform complex transformations, and Amazon Redshift is the target. Option B is wrong because Kinesis Data Streams is for streaming data. Option E is wrong because Athena is for querying, not ETL.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 13

A company uses AWS Glue to process CSV files stored in Amazon S3. The data pipeline runs daily, but recently some jobs have failed with a 'MemoryError'. The data volume has grown from 1 GB to 10 GB per day. What is the MOST cost-effective solution to resolve this issue?

Accepted Answer

Increase the number of DPUs (Data Processing Units) allocated to the Glue job.. Option C is correct because increasing the number of DPUs allows Glue to handle larger data volumes without changing the job logic. Option A is wrong because switching to a different file format does not address memory limits. Option B is wrong because moving to Amazon EMR is more complex and costly for a simple ETL job. Option D is wrong because additional memory is provisioned via DPUs, not by changing the worker type to 'G.2X' which is for machine learning.

Answer

Change the Glue worker type from Standard to G.2X.

Answer

Convert the CSV files to Parquet format using an S3 batch operation.

Answer

Migrate the job to Amazon EMR with a larger cluster.

Question 14

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then processed by an AWS Lambda function that transforms the records and writes them to an Amazon S3 bucket. Recently, the Lambda function has been timing out and the S3 bucket is not receiving all expected data. The Kinesis stream is not throttling and has sufficient shards. Which step should the company take to resolve this issue?

Accepted Answer

Increase the Lambda function's timeout and memory allocation.. Option B is correct because increasing the Lambda function's timeout and memory allows it to handle larger or more frequent records within the execution duration. Option A is wrong because Kinesis is not the bottleneck. Option C is wrong because increasing shards does not address Lambda timeouts. Option D is wrong because there is no mention of Lambda hitting concurrency limits.

Answer

Increase the Lambda function's reserved concurrency.

Answer

Increase the number of shards in the Kinesis stream.

Answer

Enable enhanced fan-out on the Kinesis stream to reduce latency.

Question 15

A financial services company is ingesting real-time stock trade data into Amazon Kinesis Data Streams. The data is then processed by a Kinesis Data Analytics application for fraud detection. The company must ensure that the data is processed in the correct order for each stock symbol. Which TWO configuration steps should be taken? (Choose two.)

Accepted Answer

Use the stock symbol as the partition key when putting records into the stream.. A is correct because using a partition key of stock symbol ensures records with the same symbol go to the same shard, preserving order. D is correct because enabling the Kinesis Client Library (KCL) with sequence number ordering ensures records are processed in order within a shard. B is wrong because increasing shards does not guarantee ordering. C is wrong because using a random partition key would distribute records across shards, breaking order. E is wrong because DynamoDB Streams is for CDC, not for streaming order.

Answer

Use a random partition key to distribute the load evenly.

Answer

Increase the number of shards to reduce latency.

Answer

Use AWS Lambda with DynamoDB Streams instead of Kinesis Data Analytics.

Question 16

A data engineering team uses AWS Glue to extract, transform, and load (ETL) data from Amazon RDS for MySQL to Amazon S3. The job runs daily and processes incremental data. The team notices that the job is taking longer than expected. Which TWO actions can improve the job performance? (Choose two.)

Accepted Answer

Use pushdown predicates to filter data at the source.. B is correct because increasing the number of DPUs (Data Processing Units) allocates more resources to the Glue job, speeding it up. D is correct because using pushdown predicates filters data at the source, reducing the amount of data transferred. A is wrong because writing to S3 with no compression increases I/O. C is wrong because using a single node (Standard) reduces parallelism. E is wrong because adding more transformations increases processing time.

Answer

Change the worker type to Standard (single node).

Answer

Add more transformations to the ETL script to clean data.

Answer

Disable compression on the output data to reduce CPU usage.

Question 17

A company needs to transform JSON data from an Amazon S3 bucket into Parquet format and load it into an Amazon Redshift cluster. The transformation includes joining with a reference table stored in Amazon RDS. Which AWS service is BEST suited for this task?

Accepted Answer

AWS Glue ETL job. Option D is correct because AWS Glue ETL jobs can read from S3, connect to RDS via JDBC, perform joins and transformations, and write to Redshift. Option A (Athena) can query S3 but cannot join with RDS natively. Option B (EMR) is possible but more complex to set up. Option C (Data Pipeline) is older and less integrated.

Answer

AWS Data Pipeline

Answer

Amazon Athena

Answer

Amazon EMR with Spark

Question 18

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an Amazon S3 bucket. Recently, the application has been failing with 'ResourceNotFoundException' for the S3 bucket. What is the MOST likely cause?

Accepted Answer

The IAM role used by the application does not have s3:PutObject permission.. Option C is correct because Kinesis Data Analytics needs permissions to write to the S3 bucket; if the IAM role lacks s3:PutObject, the error occurs. Option A is wrong because the bucket name is used, not ARN. Option B is wrong because S3 bucket policies do not require versioning. Option D is wrong because Flink application code does not configure S3 bucket region; it's determined by the bucket location.

Answer

The S3 bucket ARN is incorrectly specified in the application configuration.

Answer

The Flink application code specifies the wrong AWS Region for the S3 bucket.

Answer

The S3 bucket has versioning disabled.

Question 19

A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The data contains nested fields. Which Glue feature should the engineer use to define the schema and handle the nested structure?

Accepted Answer

Use the 'Relationalize' transform in a Glue ETL script.. Option A is correct because Glue's built-in transform 'Relationalize' converts nested JSON into flat tables. Option B is wrong because 'FindMatches' is for deduplication. Option C is wrong because 'Spigot' is for sampling. Option D is wrong because 'DropFields' is for removing fields, not handling nesting.

Answer

Use the 'FindMatches' transform to identify duplicates.

Answer

Use the 'DropFields' transform to remove nested fields.

Answer

Use the 'Spigot' transform to write sample data.

Question 20

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream, performs a 1-minute tumbling window aggregation, and writes results to an S3 bucket. Recently, the application started experiencing checkpoint failures and increasing processing delay. Which action should the engineer take FIRST to diagnose the issue?

Accepted Answer

Monitor CPU and memory utilization of the Flink application using Amazon CloudWatch metrics.. Option B is correct because checkpoint failures are often due to insufficient resources (CPU/memory) for the Flink job. Monitoring CPU and memory utilization via CloudWatch metrics directly helps identify resource bottlenecks. Option A (checkpoint interval) might help but is not diagnostic. Option C (parallelism) is a tuning step. Option D (KCL) is not relevant for Flink. The first step is to check resource utilization.

Answer

Increase the parallelism of the Flink application.

Answer

Switch to the Kinesis Client Library (KCL) for checkpointing.

Answer

Increase the checkpoint interval to reduce checkpoint frequency.