DEA-C01 Practice Test 27 — 15 Questions

Question 1

A company stores sensitive data in Amazon S3. The security team requires encryption at rest and that the encryption keys are managed by the company using AWS KMS. The data is frequently accessed by multiple AWS services. Which THREE steps should be taken to meet these requirements?

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

Accepted Answer

Configure the KMS key policy to allow the necessary AWS services to use the key for decryption. The security team requires that encryption keys be managed by the company using AWS KMS, and that multiple AWS services can access the data. To allow those services to decrypt objects encrypted with a customer-managed KMS key, the KMS key policy must explicitly grant the necessary AWS services (e.g., AWS Lambda, Amazon Athena) permission to use the key for decryption (kms:Decrypt). Without this policy, even if the bucket is configured for SSE-KMS, the services will fail to read the encrypted objects.

Answer

Use client-side encryption with the KMS key before uploading to S3

Answer

Enable default encryption on the S3 bucket using SSE-S3

Question 2

A data engineer is tasked with setting up a data pipeline that moves data from an on-premises Oracle database to Amazon S3 every hour. The network bandwidth is limited, and the engineer needs to ensure data consistency. Which AWS service should the engineer use?

Accepted Answer

AWS Database Migration Service (DMS) with change data capture (CDC).. AWS DMS with CDC is the correct choice because it can continuously replicate ongoing changes from an on-premises Oracle database to Amazon S3 while ensuring data consistency. CDC captures only the incremental changes (inserts, updates, deletes) after an initial full load, minimizing the data transferred over limited bandwidth and maintaining transactional integrity.

Answer

AWS DataSync.

Answer

Amazon Kinesis Data Firehose.

Answer

S3 Transfer Acceleration.

Question 3

A company runs a Redshift cluster for analytics. The data engineering team notices that COPY commands from S3 are failing for large files (>1 GB) with the error 'S3ServiceException: SlowDown'. What is the most effective solution?

Accepted Answer

Split the large files into smaller parts (e.g., 100 MB each) and use parallel COPY.. The 'SlowDown' error from S3 occurs when too many requests are made to the same S3 prefix, causing throttling. Splitting large files into smaller parts (e.g., 100 MB) and using parallel COPY distributes the load across multiple slices in Redshift, reducing the request rate per prefix and avoiding throttling.

Answer

Use Redshift Spectrum to query the data directly in S3.

Answer

Enable automatic compression on the target tables.

Answer

Increase the number of Redshift nodes to distribute the load.

Question 4

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 5

A data engineering team uses AWS Glue ETL jobs to process data daily. They notice that job run times are increasing as data volume grows. Which action will most effectively improve performance without changing the code?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs (Data Processing Units) for the Glue job directly scales the compute resources allocated to the job, which can improve performance and reduce run times as data volume grows. This does not require code changes. Job bookmarks help skip already processed data but do not address increasing data volume. Smaller instance types and more files in S3 may not effectively improve performance.

Answer

Use a smaller instance type for the Glue job.

Answer

Enable job bookmark to skip previously processed data.

Answer

Split the data into more files in S3.

Question 6

A company is building a data pipeline that ingests streaming data from IoT devices. The data must be stored in a durable, scalable, and cost-effective manner for batch processing. Which TWO AWS services should be used together?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams (B) is the correct ingestion service for streaming IoT data because it provides a durable, scalable, and real-time data streaming platform that can capture and store data records for up to 365 days. Amazon S3 (E) is the correct storage service for batch processing because it offers virtually unlimited durability (99.999999999%), cost-effective tiered storage, and native integration with batch processing frameworks like Amazon EMR and AWS Glue. Together, they form a classic streaming-to-batch pipeline: Kinesis ingests and buffers the streaming data, which is then persisted in S3 for downstream batch analytics.

Answer

Amazon ElastiCache

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 7

An Amazon Kinesis Data Streams application is lagging behind. The data records are small (1 KB) and the shard count is 10. The consumer uses the KCL with default configuration. Which action will MOST effectively reduce the consumer lag?

Accepted Answer

Use Enhanced Fan-Out to provide dedicated throughput.. Enhanced Fan-Out provides each consumer with dedicated throughput (2 MB/s per shard) and eliminates the need for polling, which reduces latency and lag. Given that the consumer is lagging with default configuration and small records, Enhanced Fan-Out directly addresses the consumer-side bottleneck by providing dedicated read throughput, making it the most effective option. Option A is incorrect because the KCL does not support multiple workers per shard; each shard is processed by a single worker in a single-threaded manner. Option C may help if the shard is saturated with incoming data, but the problem is consumer lag, not data ingestion. Option D reduces data size but does not address the consumer processing speed.

Answer

Increase the number of KCL workers per shard (e.g., 2 workers per shard).

Answer

Increase the number of shards to 20.

Answer

Reduce the record size by compressing the data.

Question 8

A data engineer is troubleshooting an AWS Glue job that writes data to an S3 bucket. The IAM role attached to the Glue job has the policy shown in the exhibit. The job fails when writing to the 'secrets/' prefix but succeeds when writing to other prefixes. What is the reason for the failure?

Accepted Answer

The Deny statement explicitly denies PutObject to the secrets/ prefix.. The IAM policy attached to the Glue job contains an explicit Deny statement for the 'secrets/' prefix, which overrides any Allow. In AWS IAM, an explicit Deny always takes precedence over Allow. Therefore, the job fails when writing to 'secrets/' but succeeds for other prefixes where no Deny exists.

Answer

The job does not have permission to write to the bucket at all.

Answer

The resource ARN in the Allow statement does not include the bucket itself.

Answer

The Deny statement is not effective because it is placed after the Allow.

Question 9

A company runs a data pipeline that uses AWS Glue to process data from an Amazon DynamoDB table and write results to Amazon S3. The Glue job runs on a schedule every hour. Recently, the job started failing intermittently with 'ProvisionedThroughputExceededException' errors from DynamoDB. What is the BEST solution?

Accepted Answer

Implement exponential backoff and retries in the Glue job for DynamoDB operations.. ProvisionedThroughputExceededException is a throttling error that occurs when request rate exceeds the table's provisioned read/write capacity, and AWS's documented best practice is to implement exponential backoff and retry logic so the client retries with increasing delays instead of hammering the table. This is the correct fix for intermittent throttling because it lets the Glue job ride out short bursts of capacity contention without failing. It also works whether the table is in provisioned or on-demand mode, and it is the least disruptive, most cost-effective change.

Answer

Use DynamoDB Accelerator (DAX) to reduce read latency.

Answer

Change the Glue job schedule to run every 2 hours.

Answer

Increase the read capacity units of the DynamoDB table.

Question 10

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The migration must have minimal downtime. Which service should be used to perform the migration?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it supports continuous replication from MongoDB to Amazon DocumentDB using change data capture (CDC), enabling near-zero downtime migrations. DMS can perform a full load of existing data and then apply ongoing changes from the source MongoDB oplog, keeping the target DocumentDB synchronized until the cutover.

Answer

AWS Glue

Answer

AWS DataSync

Answer

Amazon S3 Transfer Acceleration

Question 11

Which TWO of the following are features of Amazon RDS Multi-AZ deployments? (Choose 2.)

Accepted Answer

Automatic failover to a standby instance in case of an AZ failure.. Amazon RDS Multi-AZ deployments automatically handle failover to a standby instance in a different Availability Zone when the primary instance fails or the AZ becomes unavailable. This synchronous replication ensures zero data loss and minimal downtime, with the standby instance automatically promoted to primary without manual intervention.

Answer

Read replicas in the same region for offloading read traffic.

Answer

Automatic storage scaling based on usage.

Answer

Synchronous replication across AWS Regions.

Question 12

A company needs to ensure that data stored in Amazon RDS is encrypted at rest. Which action should the data engineer take?

Accepted Answer

Create a new RDS instance with encryption enabled using AWS KMS.. Amazon RDS encryption at rest must be enabled when the DB instance is created, using AWS KMS. It cannot be added later. Option A is incorrect because encryption cannot be enabled on an existing RDS instance; you must create a new one with encryption enabled. Option B is incorrect because encrypting the underlying EBS volumes does not encrypt the RDS database; RDS encryption at rest is separate and must be configured at the instance level. Option D is incorrect because SSL/TLS secures data in transit, not at rest.

Answer

Enable encryption at rest by modifying the existing RDS instance.

Answer

Encrypt the underlying EBS volumes using AWS KMS.

Answer

Enable SSL/TLS for connections to the RDS instance.

Question 13

A data engineering team is designing a batch processing workflow using AWS Glue. The job reads from an S3 bucket, transforms data, and writes to another S3 bucket. The job runs daily and processes new data incrementally. Which THREE features should they use to optimize performance and cost?

Accepted Answer

Enable Glue job autoscaling.. Option B is correct because Glue job autoscaling dynamically adjusts the number of workers (DPUs) based on the workload, which reduces cost during lighter stages and improves throughput during heavier ones for a daily incremental job. Option D is correct because predicate pushdown and column pruning let Glue read only the needed partitions, rows, and columns from Parquet/ORC on S3, cutting I/O, shuffle, and DPU-hours. Option E is correct because Glue job bookmarks persist state so the job processes only new or changed data since the last run, which is exactly the incremental daily pattern described and avoids reprocessing old S3 objects. Option A is not marked correct because converting all input data to Parquet is a broad storage-format change, not a Glue job performance/cost feature, and it may add conversion overhead. Option C is not marked correct because manually increasing DPUs for every run is static over-provisioning that raises cost rather than optimizing it, unlike autoscaling.

Answer

Convert all input data to Parquet format before processing.

Answer

Manually increase the number of DPUs for each run.

Question 14

A data engineer is designing a data ingestion pipeline to load millions of small JSON files from an on-premises FTP server into Amazon S3. The pipeline should minimize cost and operational overhead. Which approach is most suitable?

Accepted Answer

Deploy AWS DataSync to transfer files from the FTP server to S3. AWS DataSync is the most suitable option because it is designed to efficiently transfer large volumes of data from on-premises storage (including FTP servers) to AWS, handling millions of small files with minimal operational overhead. It automates data transfer, retries, and validation, and it is cost-effective as you pay only for the data transferred, with no need for additional infrastructure or complex scripting.

Answer

Use S3 Transfer Acceleration to upload files directly from the FTP server

Answer

Use AWS Snowball Edge to ship the data to AWS

Answer

Set up an AWS Direct Connect connection and use AWS CLI to copy files

Question 15

A company needs to ingest data from multiple on-premises databases into Amazon S3 for analytics. The databases include Oracle, MySQL, and PostgreSQL. The data must be continuously replicated with minimal latency. Which AWS service should be used?

Accepted Answer

AWS Database Migration Service (AWS DMS). AWS DMS supports continuous replication (change data capture, CDC) from Oracle, MySQL, and PostgreSQL to S3 as a target, enabling near-real-time data ingestion with minimal latency. It handles schema conversion and can replicate ongoing changes without interrupting source databases, making it the correct choice for this use case.

Answer

Amazon Kinesis Data Streams

Answer

AWS Snowball

Answer

AWS Glue