DEA-C01 Practice Test 33 — 15 Questions

Question 1

A company uses Amazon Kinesis Data Streams with a shard count of 10 to ingest clickstream data. The data is consumed by a Lambda function that transforms the records and writes to Amazon S3. Recently, the Lambda function started failing with 'ProvisionedThroughputExceededException' errors. The average record size is 5 KB, and the incoming data rate is 15 MB/s. What is the most likely cause and solution?

Accepted Answer

Increase the number of shards in the Kinesis data stream to 15.. Option B is correct because with 10 shards, the total write capacity is 10 MB/s (1 MB/s per shard). The incoming rate is 15 MB/s, exceeding capacity. Increasing shards to 15 would provide 15 MB/s write capacity. Option A is wrong because Lambda concurrency is not the issue; the error is about throughput exceeded. Option C is wrong because increasing Lambda memory does not affect Kinesis write limits. Option D is wrong because the error is on the producer side, not the consumer.

Answer

Decrease the batch size of the Lambda event source mapping.

Answer

Increase the Lambda function's memory allocation to 3008 MB.

Answer

Increase the Lambda function's reserved concurrency.

Question 2

A data engineer is ingesting streaming data from thousands of IoT devices into AWS. The data is JSON-formatted and must be stored in Amazon S3 for long-term analytics. Which service is most appropriate for real-time ingestion and routing to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the easiest way to load streaming data into S3. Kinesis Data Streams requires custom consumers; AWS Glue is batch ETL; Amazon SQS is not designed for direct S3 delivery.

Answer

Amazon SQS

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Question 3

A data engineer is designing a data ingestion pipeline for real-time clickstream data. The data must be available for both real-time analytics and batch processing. The engineer wants to use Amazon Kinesis Data Streams. Which THREE components should be included in the architecture?

Accepted Answer

Amazon Kinesis Data Streams. Kinesis Data Streams ingests data, Kinesis Client Library (KCL) consumes for real-time analytics, and Kinesis Data Firehose delivers to S3 for batch. Kinesis Data Analytics is for real-time SQL, not batch; SQS is not used; Glue is batch but not directly from stream.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon SQS as a buffer

Question 4

A company needs to ingest data from multiple SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for centralized analytics. The data volume is several GB per day. Which AWS service is most suitable for this ingestion?

Accepted Answer

AWS Glue. Option B (AWS Glue) is correct because Glue can connect to various data sources using crawlers and ETL jobs, and it supports JDBC connections to SaaS databases. Option A (Amazon Athena) is for querying, not ingestion. Option C (Amazon Kinesis) is for streaming, not batch from SaaS. Option D (AWS Data Pipeline) is older and less flexible than Glue.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Athena

Answer

AWS Data Pipeline

Question 5

A data engineer needs to design a data ingestion pipeline that captures change data capture (CDC) events from an on-premises SQL Server database to Amazon S3 with low latency. The pipeline must handle schema changes and ensure exactly-once delivery semantics. Which combination of AWS services should the engineer use?

Accepted Answer

AWS Database Migration Service (DMS) with Amazon Kinesis Data Firehose to Amazon S3. Option C is correct because AWS Database Migration Service (DMS) with ongoing replication captures CDC events from on-premises SQL Server and can write to S3. Amazon Kinesis Data Firehose then delivers the data to S3 with buffering, and using a custom prefix with partition keys can help handle schema changes. Option A (Kinesis Data Streams) is not directly compatible with SQL Server CDC. Option B (AppFlow) is for SaaS applications, not on-premises databases. Option D (Lambda direct to S3) would require custom CDC implementation.

Answer

AWS AppFlow with SQL Server connector to Amazon S3

Answer

AWS Glue ETL job with JDBC connection to SQL Server and writing to Amazon S3

Answer

Amazon Kinesis Data Streams with AWS Lambda consumer writing to Amazon S3

Question 6

Refer to the exhibit. A data engineer is troubleshooting an AWS Glue ETL job that fails with an access denied error when writing to S3. The IAM role attached to the Glue job has the policy shown. What is the most likely cause of the error?

Accepted Answer

The Glue job is writing to an S3 bucket that is not included in the Resource ARN. Option B is correct because the policy only allows s3:PutObject on s3://my-data-bucket/*, but if the job writes to a different bucket or a path that doesn't exist, it may fail. Option A (KMS) is plausible but not indicated. Option C (Glue StartJobRun) is allowed. Option D (S3 list) is not needed for writing.

Answer

The Glue job is missing the s3:ListBucket permission on the bucket

Answer

The S3 bucket is encrypted with AWS KMS and the policy does not include kms:Decrypt permissions

Answer

The Glue job does not have permission to call glue:StartJobRun

Question 7

A company uses AWS Glue ETL to transform data from Amazon RDS for MySQL to Amazon S3. The Glue job reads from a JDBC connection. The job runs once daily and processes all records, but the data volume is growing. Which change would improve performance and reduce costs?

Accepted Answer

Enable Glue job bookmarking and set the job to process only new data. Using incremental extraction with bookmarking allows Glue to process only new data instead of full scans, reducing time and cost.

Answer

Increase the number of DPUs for the Glue job

Answer

Switch to a Glue Python shell job

Answer

Use a higher JDBC fetch size

Question 8

Refer to the exhibit. A data engineer is attaching this IAM policy to an IAM role used by an AWS Glue job. The job reads from a Kinesis Data Streams stream and writes transformed data to an S3 bucket. When the job runs, it fails with an AccessDenied error for the Kinesis stream. What is the MOST likely cause?

Accepted Answer

The IAM policy is missing the 'kinesis:DescribeStream' action.. Option B is correct because missing 'kinesis:DescribeStream' is required for Glue to read from the stream. Option A is wrong because the S3 policy is fine. Option C is wrong because resource ARN is correctly formatted. Option D is wrong because the policy does not allow 'kinesis:GetRecords' or 'kinesis:DescribeStream'.

Answer

The stream ARN in the policy is incorrect.

Answer

The Glue job does not have permissions to call 'kinesis:PutRecord'.

Answer

The S3 bucket policy blocks the PutObject action from the Glue role.

Question 9

A company uses AWS Glue to transform data in S3. The transformation job reads Parquet files, filters rows, and writes to another S3 bucket. The job takes longer than expected. Which change would MOST likely reduce the job execution time?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Option B is correct because AWS Glue jobs can benefit from increased DPU allocation for parallel processing. Option A is wrong because converting to CSV would increase size and likely slow processing. Option C is wrong because reducing the number of partitions reduces parallelism, increasing time. Option D is wrong because using a single file does not improve parallelism.

Answer

Use a single large file instead of multiple small files.

Answer

Reduce the number of partitions in the output data.

Answer

Convert the input files from Parquet to CSV format.

Question 10

A data engineer is designing a data pipeline that ingests CSV files from an FTP server to Amazon S3. The files arrive hourly and each file is about 500 MB. The engineer wants to minimize operational overhead and cost. Which approach is best?

Accepted Answer

Use AWS Transfer for SFTP and point the endpoint to an S3 bucket. AWS Transfer for SFTP provides a fully managed FTP service that writes directly to S3, eliminating the need to manage servers. Lambda with boto3 is code-heavy; EC2 requires management; Snowball is for large offline transfers.

Answer

Write a Python script in AWS Lambda using boto3 to download from FTP and upload to S3

Answer

Use AWS Snowball Edge to transfer files weekly

Answer

Deploy an Amazon EC2 instance with a cron job to run wget and aws s3 cp

Question 11

Refer to the exhibit. An IAM policy for an AWS Lambda function. The Lambda function is triggered by an S3 event (object created) and needs to read from a Kinesis stream. However, the function fails with access denied when trying to read from Kinesis. What is the most likely cause?

Accepted Answer

The Kinesis stream is encrypted with a customer managed KMS key, and the Lambda function lacks kms:Decrypt permission. The Lambda function lacks permission to list the Kinesis stream (kinesis:ListStreams). The policy only allows DescribeStream, GetShardIterator, and GetRecords. To read from a stream, the initial DescribeStream call works, but the Lambda execution role may need additional actions depending on the SDK. However, often the error is due to missing kinesis:ListStreams if the SDK lists streams first. But more commonly, the policy is correct; the failure might be due to missing s3:ListBucket? No, the error is Kinesis. Actually, the policy allows GetRecords, so the most likely cause is that the Kinesis stream is encrypted with a KMS key and the Lambda function lacks kms:Decrypt permission. This is a common scenario. The policy does not include KMS permissions.

Answer

The Lambda function is not in the same region as the Kinesis stream

Answer

The Lambda function does not have permission to list S3 buckets

Answer

The S3 bucket policy denies access to the Lambda function

Question 12

A company ingests JSON logs into Amazon S3 using Kinesis Data Firehose. The logs contain a timestamp field, but the delivery to S3 is delayed by up to 15 minutes during peak hours. The business requires near-real-time availability (under 2 minutes). Which configuration change should the data engineer make?

Accepted Answer

Decrease the buffer interval to 60 seconds. Option B is correct because reducing the buffer interval to 60 seconds (minimum 60s for Firehose) forces Firehose to deliver data more frequently, reducing latency. Option A (increasing buffer size) would increase latency. Option C (increasing shards) does not apply to Firehose directly; shards are for Kinesis Data Streams. Option D (buffering hints) is not a direct configuration in Firehose.

Answer

Increase the number of shards in the Kinesis Data Firehose stream

Answer

Increase the buffer size to 128 MB

Answer

Enable buffering hints in the Firehose delivery stream

Question 13

Refer to the exhibit. A data engineer is troubleshooting a Kinesis Data Streams consumer that is falling behind. The stream has 2 shards and is receiving data at a rate of 2 MB/s. The consumer is an AWS Lambda function with a batch size of 100 records. What should the engineer do to improve consumer throughput?

Accepted Answer

Increase the number of shards in the stream to 4. Option A is correct because the stream is at its write limit (2 MB/s, but each shard supports 1 MB/s input, so 2 shards = 2 MB/s, but the consumer may need more read capacity. Increasing the number of shards increases both write and read capacity. Option B (Lambda memory) may help but not as effective. Option C (increasing retention) does not help throughput. Option D (decreasing batch size) reduces throughput.

Answer

Decrease the Lambda batch size to 10 records

Answer

Increase the retention period of the stream to 168 hours

Answer

Increase the memory allocation of the Lambda function

Question 14

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for a streaming ingestion architecture? (Choose 3.)

Accepted Answer

Ability to deliver data directly to Amazon Redshift. KDS offers custom processing with shard-level control, while Firehose auto-scales and can deliver to S3, Redshift, etc. KDS requires consumer management; Firehose is managed.

Answer

Data retention period longer than 7 days

Answer

Lower cost per GB ingested

Question 15

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).. Option A is correct because using a memory-optimized instance type like r5.xlarge provides more memory per core. Option B is wrong because more partitions with same resources can cause overhead. Option C is wrong because increasing executor cores without increasing memory can worsen memory issues. Option D is wrong because Kryo serialization reduces memory for serialized objects, not OOM from processing.

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.