DEA-C01 Practice Exam 67 — 20 Questions

Question 1

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?

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

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

Question 2

A retail company uses Amazon Kinesis Data Firehose to ingest clickstream data from its website into an Amazon S3 bucket. The data includes fields: user_id, event_type, timestamp, page_url. Recently, the data engineering team noticed that some records have malformed JSON (missing commas, extra brackets) causing delivery failures to S3. The Firehose delivery stream is configured to retry failed records for 300 seconds, after which the records are sent to an S3 bucket for failed records. The team wants to transform the data to correct malformed JSON before delivery to the main S3 bucket. They need a solution that does not require managing servers and can handle high throughput. What should the team do?

Accepted Answer

Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.. AWS Lambda can be used as a data transformation function within Kinesis Data Firehose to process each record individually, fixing malformed JSON without managing servers. The Lambda function receives records in batches, transforms them (e.g., parsing and correcting JSON syntax), and returns them to Firehose for delivery to S3. This serverless approach scales automatically with high throughput. Option B (EMR with Spark) requires managing a cluster and adds latency, making it less suitable for real-time streaming. Option C (Glue streaming ETL) is serverless but introduces more complexity and potential latency compared to Firehose's built-in Lambda transformation. Option D (Kinesis Data Analytics) is designed for real-time analytics and not for record-level transformations like JSON correction.

Answer

Set up an Amazon EMR cluster with Apache Spark to process the data in micro-batches and fix JSON errors.

Answer

Use an AWS Glue streaming ETL job to read from Firehose and write corrected data to S3.

Answer

Use Amazon Kinesis Data Analytics with a SQL application to parse and fix JSON.

Question 3

A data engineer is reviewing the lifecycle configuration of an S3 bucket. The bucket stores log files. The engineer wants to ensure that objects are deleted after 365 days. What is the current behavior?

Accepted Answer

Objects are deleted after 365 days.. The lifecycle configuration is set to expire objects after 365 days, which means the S3 service will automatically delete the objects once they reach that age. Since the question states the engineer wants to ensure deletion after 365 days and the current behavior matches that, option A is correct. No transition actions are defined, so objects remain in the original storage class until expiration.

Answer

Objects are transitioned to S3 Standard-IA immediately.

Answer

Objects are transitioned to S3 Glacier after 365 days.

Answer

Noncurrent versions of objects are deleted after 365 days.

Question 4

A data engineer is troubleshooting a Kinesis Data Streams consumer application that is falling behind. The stream has 10 shards and is receiving 5 MB/s of data. The consumer uses the Kinesis Client Library (KCL) with a single worker. The worker is processing all 10 shards but is experiencing high latency and checkpointing delays. Which THREE actions should the engineer take to improve consumer performance? (Select THREE.)

Accepted Answer

Increase the number of KCL workers to match the number of shards.. The KCL worker is processing all 10 shards sequentially within a single worker, causing a bottleneck. By increasing the number of KCL workers to match the number of shards, each worker can process one shard in parallel, significantly improving throughput and reducing latency. This is a standard scaling pattern for KCL-based consumers.

Answer

Decrease the checkpoint interval to reduce checkpointing overhead.

Answer

Increase the number of shards in the stream.

Question 5

A data engineer is using Amazon DynamoDB to store session data for a web application. The engineer wants to ensure that all data is encrypted at rest using an AWS managed key. Which step should the engineer take to achieve this?

Accepted Answer

Create the DynamoDB table with encryption at rest enabled using an AWS managed key. [CORRECT]. Only option E is correct. DynamoDB encryption at rest is enabled by default for new tables using an AWS managed key (aws/dynamodb). Creating the table with encryption at rest enabled using an AWS managed key ensures all data is encrypted with a key managed by AWS. Option C is incorrect because specifying a customer managed key would override the default AWS managed key, which does not meet the requirement to use an AWS managed key. Options A, B, and D are incorrect: SSE-S3 is for S3, disabling encryption is unsafe and does not meet the requirement, and client-side encryption is separate from at-rest encryption.

Answer

Enable server-side encryption with S3-managed keys (SSE-S3) on the DynamoDB table. [wrong]

Answer

Disable encryption at rest to improve performance. [wrong]

Answer

Specify an AWS KMS customer managed key for encryption if required. [wrong]

Answer

Use client-side encryption before writing data to DynamoDB. [wrong]

Question 6

A data engineer needs to ingest real-time clickstream data from a website into Amazon S3 for analytics. The data arrives as JSON records, each under 1 KB. The engineer wants to use a serverless solution with automatic scaling and minimal operational overhead. Which AWS service should be used as the ingestion endpoint?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service designed to ingest streaming data and automatically load it into Amazon S3 with no ongoing administration. It handles automatic scaling, converts incoming JSON records to formats like Parquet or ORC if needed, and can batch data into S3 based on time or size intervals, making it ideal for real-time clickstream ingestion with minimal operational overhead.

Answer

Amazon S3 with presigned URLs

Answer

Amazon Kinesis Data Analytics

Answer

AWS Lambda function behind an API Gateway

Question 7

A data pipeline ingests CSV files from an S3 bucket into a Redshift table using the COPY command. Recently, files with inconsistent column delimiters (some use pipes, others use commas) have been arriving. The pipeline must handle both delimiters without manual intervention. What is the MOST efficient solution?

Accepted Answer

Create an AWS Lambda function triggered by S3 events that reads the first line of each file, detects the delimiter, and runs the COPY command with the appropriate DELIMITER option.. It provides an automated, event-driven solution that dynamically detects the delimiter of each incoming CSV file and executes the COPY command with the appropriate DELIMITER option. This approach eliminates manual intervention and leverages AWS Lambda's ability to process S3 events in near real-time, making it the most efficient for handling inconsistent delimiters.

Answer

Configure the COPY command with a fixed delimiter (e.g., comma) and manually convert files with pipes before ingestion.

Answer

Use AWS Glue to crawl the S3 bucket and automatically detect the schema and delimiter before writing to Redshift.

Answer

Use Amazon Athena to query the files with the OpenCSVSerDe, which automatically detects delimiters, and then write the results to Redshift.

Question 8

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format and each record is about 2 KB. The delivery stream is configured to buffer data for 60 seconds or 5 MB, whichever comes first. The team notices that the S3 objects are very small (around 1 MB) and numerous, causing high costs due to S3 PUT requests. Which configuration change should the team make to reduce the number of S3 objects?

Accepted Answer

Increase the buffer size to 50 MB and the buffer interval to 300 seconds.. Increasing the buffer size to 50 MB and the buffer interval to 300 seconds allows Kinesis Data Firehose to accumulate more data before writing to S3, resulting in fewer, larger objects. The current configuration triggers a write every 60 seconds or when 5 MB is buffered, but since each record is only 2 KB, the 5 MB threshold is rarely met, causing frequent small writes. By raising both thresholds, the delivery stream will buffer more records and write larger objects, reducing the number of S3 PUT requests and associated costs.

Answer

Enable compression (GZIP) on the delivery stream.

Answer

Reduce the buffer interval to 30 seconds and keep buffer size at 5 MB.

Answer

Switch from Kinesis Data Firehose to Amazon Kinesis Data Streams and use a Lambda function to write to S3.

Question 9

A company runs a data lake on Amazon S3 with AWS Lake Formation for access control. The data lake contains sensitive customer information. A data scientist needs to query the data using Amazon Athena. The data scientist has been granted SELECT permission on the database and tables via Lake Formation. However, when the data scientist runs a query in Athena, they receive an error: 'Access denied. Please check your permissions.' The IAM role used by Athena has the following permissions: s3:GetObject, s3:ListBucket, and lakeformation:GetDataAccess. The Lake Formation admin has verified that the data scientist is a member of a Lake Formation data lake location and has been granted 'Describe' and 'Select' permissions on the table. What is the most likely reason for the access denied error?

Accepted Answer

The S3 bucket policy does not grant the Athena IAM role access to the S3 location.. The access denied error occurs because the IAM role used by Athena lacks the necessary S3 permissions, even though Lake Formation has granted SELECT and DESCRIBE permissions. Lake Formation handles permissions at the metadata level, but the actual data access still requires S3 bucket policy or IAM policy to allow s3:GetObject and s3:ListBucket for the specific S3 location. If the bucket policy does not grant the Athena IAM role access to the S3 location, the request will be denied. Option B correctly identifies this issue. Option A is incorrect because Lake Formation tag-based access control is not required for this scenario; the data scientist already has direct table permissions. Option C is incorrect because lakeformation:GetEffectivePermissions is not needed; the role already has lakeformation:GetDataAccess. Option D is incorrect because Athena uses the IAM role assigned to the workgroup, not the data scientist's individual IAM user.

Answer

The data scientist is not assigned to the correct Lake Formation tag.

Answer

The Athena IAM role is missing lakeformation:GetEffectivePermissions permission.

Answer

The data scientist's IAM user lacks the necessary S3 permissions.

Question 10

A media company is building a data pipeline to ingest user activity logs from multiple sources into Amazon S3. The logs are JSON files generated every minute. The company wants to use Amazon Athena to query the logs with minimal latency and cost. The current approach is to use AWS Kinesis Data Firehose to deliver the logs to S3 with a prefix like 'logs/2024/01/01/00/file.json'. However, when running Athena queries, the team notices high query costs because Athena scans all files in the 'logs/' prefix even when querying for a specific date. What should the team do to reduce the amount of data scanned by Athena?

Accepted Answer

Create a Hive-style partition structure in S3 with keys like 'year=2024/month=01/day=01/hour=00/' and update the Glue Data Catalog accordingly.. Creating a Hive-style partition structure (e.g., year=2024/month=01/day=01/) enables Athena to perform partition pruning. When querying for a specific date, Athena scans only the relevant partition, reducing data scanned and cost. Option A is incorrect because Athena views do not reduce data scanning; they just store query logic. Option B is incorrect because more granular prefixes without a partition structure (like Hive-style) do not enable partition pruning in Athena; Athena treats the prefix as a folder and still scans all files. Option C is incorrect because while converting to Parquet reduces scan size due to columnar storage and compression, it does not address the lack of partitioning; the main issue is full scan of all files regardless of format.

Answer

Create an Athena view that filters by date.

Answer

Increase the number of partitions by using a more granular prefix like 'logs/2024/01/01/00/00/'.

Answer

Convert the JSON files to Apache Parquet format using AWS Glue ETL jobs.

Question 11

A data engineer is troubleshooting slow query performance on an Amazon Redshift cluster. The cluster has 10 nodes and is using automatic distribution style. The engineer suspects that data distribution is causing excessive data movement. Which steps should the engineer take to diagnose and resolve the issue? (Choose THREE.)

Accepted Answer

Choose appropriate distribution keys for large tables. Choosing appropriate distribution keys for large tables ensures that data is evenly distributed across the cluster slices, minimizing the need for data redistribution during joins and aggregations. Automatic distribution style may not always select the optimal key, leading to excessive data movement and slow query performance.

Answer

Run the VACUUM command to reclaim space

Answer

Increase the number of nodes in the cluster

Question 12

A company uses Amazon EMR to run Spark jobs on a cluster of 20 nodes. The cluster stores intermediate data on Amazon S3 using EMRFS. The company's data engineering team notices that the Spark jobs are running slower than expected. Upon investigating, they find that the cluster is experiencing high network I/O and that the S3 storage costs have increased significantly. The team suspects that the Spark jobs are writing too much intermediate data to S3. The jobs are performing many shuffle operations. The team wants to optimize the job performance and reduce costs without modifying the Spark application code. What should the data engineer do?

Accepted Answer

Configure the EMR cluster to use instance store volumes for intermediate data instead of EMRFS.. Configuring the EMR cluster to use instance store volumes for intermediate data instead of EMRFS reduces the amount of data written to Amazon S3 during shuffle operations. Instance store volumes provide local, ephemeral storage that is faster and avoids network I/O and S3 costs associated with EMRFS. This directly addresses the high network I/O and increased S3 storage costs without modifying the Spark application code. Option A is incorrect because enabling S3 server-side encryption does not affect performance or reduce the volume of data written; it only encrypts data at rest. Option B is incorrect because increasing the size of EBS root volumes does not change how shuffle data is stored; EMR uses instance store or EMRFS for shuffle, not EBS root volumes. Option D is incorrect because adding more nodes may increase network I/O and cost, and does not prevent the job from writing intermediate data to S3.

Answer

Enable S3 server-side encryption on the S3 bucket to reduce storage costs.

Answer

Increase the size of the EBS root volumes on the cluster nodes to store more intermediate data locally.

Answer

Add more nodes to the cluster to distribute the shuffle load.

Question 13

A company needs to tag all resources created in a specific AWS account to enforce data governance policies. Which AWS service can automatically enforce tagging rules?

Accepted Answer

AWS Organizations SCPs. (AWS Organizations SCPs) is correct. Service Control Policies (SCPs) can be used to enforce tagging rules across all accounts in an organization by denying actions that don't meet tagging requirements. Option B (AWS Service Catalog) is used for creating and managing approved IT services, not for enforcing tagging rules. Option C (AWS Resource Access Manager) is for sharing resources across accounts, not for enforcement. Option D (AWS Systems Manager) is for operational management, not for governance policies.

Answer

AWS Service Catalog

Answer

AWS Resource Access Manager

Answer

AWS Systems Manager

Question 14

Which THREE of the following are valid storage classes in Amazon S3? (Choose THREE.)

Accepted Answer

S3 Standard. S3 Standard is a valid storage class designed for frequently accessed data with low latency and high throughput. It offers 99.999999999% durability and 99.99% availability, making it suitable for a wide range of use cases like cloud applications, dynamic websites, and content distribution.

Answer

S3 Archive

Answer

S3 Cold

Question 15

A data engineering team uses AWS Glue ETL jobs to process data from Amazon S3 and load it into an Amazon Redshift cluster. The cluster has a single node of type dc2.large. The team notices that the ETL jobs are failing intermittently with errors related to disk space. The Redshift cluster shows that the disk is nearly full. The team needs to resolve the disk space issue and ensure the ETL jobs can complete successfully without increasing costs significantly. Which solution should the team implement?

Accepted Answer

Convert the cluster to use RA3 node types (e.g., ra3.xlarge) with managed storage.. Converting to RA3 node types with managed storage allows compute and storage to scale independently, resolving the disk space issue without over-provisioning compute. Options B and D do not increase disk capacity. Option C adds more nodes but still uses fixed storage per node and increases cost significantly.

Answer

Load data into a staging table first, then perform a VACUUM and ANALYZE on the target tables.

Answer

Add more nodes to the Redshift cluster by resizing to a multi-node dc2.large configuration.

Answer

Set the table's distribution style to ALL for fact tables to avoid data redistribution during joins.

Question 16

A logistics company uses AWS Glue to process GPS data from delivery trucks. The data is stored in Amazon S3 as JSON files. The Glue job reads the JSON files, converts them to Parquet, and writes them back to S3. The company notices that the Glue job takes too long to complete. The data engineer wants to improve the job's performance without changing the code. What should the data engineer do?

Accepted Answer

Change the worker type to G.2X.. Changing the worker type to G.2X provides more memory and CPU per worker, which improves performance for memory-intensive tasks like converting JSON to Parquet. Option A is wrong because increasing the number of DPUs can help with parallelism but may still be limited by per-worker memory; upgrading worker type is more efficient. Option C is wrong because G.1X is the default and provides less resources than G.2X. Option D is wrong because decreasing DPUs would reduce parallelism and worsen performance.

Answer

Increase the number of DPUs to 20.

Answer

Change the worker type to G.1X.

Answer

Decrease the number of DPUs to 5 to reduce overhead.

Question 17

A gaming company ingests player event data from mobile games into Amazon Kinesis Data Streams. Each event is a small JSON payload (<1 KB). The data must be delivered to Amazon S3 for analytics, and the company wants to minimize storage costs by aggregating events into larger files (e.g., 100 MB per file). The current setup uses Kinesis Data Firehose with a buffer size of 10 MB and a buffer interval of 60 seconds, but the resulting files are very small (average 5 MB) because the data volume is low. The engineer needs to ensure that files are at least 100 MB to reduce the number of S3 objects and lower costs. What should the engineer do?

Accepted Answer

Increase the buffer size in Kinesis Data Firehose to 100 MB and increase the buffer interval to 300 seconds.. Increase the buffer size in Kinesis Data Firehose to 100 MB and increase the buffer interval to 300 seconds. This allows more data to accumulate before writing to S3, resulting in larger files (≥100 MB) and reducing the number of S3 objects, thus lowering storage costs. Option B (Lambda) would require custom buffering logic and may encounter timeouts or memory limits. Option A (Glue streaming ETL) adds latency and cost without directly controlling file size. Option D (EMR Spark Streaming) is overkill for this simple aggregation need.

Answer

Use an AWS Glue streaming ETL job with a 100 MB file size threshold to write to S3.

Answer

Use an AWS Lambda function to buffer events in memory and write to S3 when buffer reaches 100 MB.

Answer

Use Amazon EMR with Spark Streaming to aggregate and write larger files to S3.

Question 18

A financial services company is building a real-time fraud detection system. Transaction data is ingested via Amazon Kinesis Data Streams and processed by an Amazon Kinesis Data Analytics for Apache Flink application that runs sliding window aggregations. The output is written to an Amazon S3 bucket for downstream analysis. The Flink application is configured with parallelism of 4 and checkpointing every minute. The company has noticed that the application is experiencing high latency and the checkpointing is frequently failing. The CloudWatch metrics show that the Flink application's CPU utilization is near 100% and the checkpoint duration is spiking to over 5 minutes. The data engineer needs to improve performance. Which action should the data engineer take?

Accepted Answer

Increase the parallelism of the Flink application to distribute the workload across more resources.. Increasing the parallelism of the Flink application allows the workload to be distributed across more resources, which reduces CPU pressure and checkpoint duration. The high CPU utilization and checkpoint spikes indicate that the current parallelism (4) is insufficient for the data volume. Option A is incorrect because increasing shards in the source stream without increasing parallelism may not help if the bottleneck is processing capacity, not ingestion throughput. Option C is incorrect while increasing heap memory might help with state size, the primary issue here is CPU saturation, not memory. Option D is incorrect because decreasing the checkpoint interval would increase checkpoint frequency, potentially worsening failures and latency.

Answer

Increase the number of shards in the source Kinesis stream to improve throughput.

Answer

Increase the heap memory of the Flink application to handle larger state.

Answer

Decrease the checkpoint interval to 30 seconds to reduce the amount of state being checkpointed.

Question 19

A data engineering team notices that an Amazon Kinesis Data Stream is frequently exceeding its shard write throughput limit, causing throttling. The team needs a long-term solution to handle variable write traffic without manual intervention. Which action should the team take?

Accepted Answer

Implement a buffer using Amazon S3 and AWS Lambda that aggregates records and writes to Kinesis in batches.. Using Amazon S3 and AWS Lambda to buffer and batch records before writing to Kinesis smooths out traffic spikes, reducing throttling without manual intervention. Option A is incorrect because the Kinesis Client Library (KCL) is used by consumers, not producers; it cannot throttle write throughput. Option B is incorrect because manually increasing shards during peak hours requires manual intervention and is not a long-term automated solution. Option C is incorrect because Amazon Kinesis Data Firehose is designed to deliver streaming data to destinations like S3, Redshift, or Elasticsearch, not to buffer records before writing to a Kinesis stream.

Answer

Configure the Kinesis Client Library to throttle consumption.

Answer

Increase the number of shards manually during peak hours.

Answer

Use Amazon Kinesis Data Firehose to buffer records before delivery to the stream.

Question 20

A company has a Kinesis Data Firehose delivery stream that receives JSON data from IoT devices. The data is delivered to an S3 bucket. The company notices that the data in S3 is delayed by up to 30 minutes. The Firehose stream is configured with a buffer size of 1 MB and a buffer interval of 60 seconds. The incoming data rate is approximately 100 KB per second. The company needs to reduce the delivery latency to under 5 minutes. Which action should the company take?

Accepted Answer

Decrease the buffer size to 256 KB.. The observed latency of up to 30 minutes is likely due to the buffer size being too large relative to the data rate, causing long waits to fill the buffer. With a data rate of 100 KB/s and a buffer size of 1 MB, the buffer fills in approximately 10 seconds, but the buffer interval of 60 seconds already limits delivery to at most 60 seconds. However, the 30-minute delay suggests additional issues such as backlog or configuration errors. Decreasing the buffer size to 256 KB will cause more frequent deliveries (every ~2.5 seconds), reducing latency. Option A (Lambda transformation) adds processing time and increases latency. Option B (increase buffer interval to 300 seconds) would increase latency. Option C (change compression) does not affect delivery frequency. Therefore, option D is correct.

Answer

Enable Lambda transformation to process data faster.

Answer

Increase the buffer interval to 300 seconds.

Answer

Change the compression format from GZIP to Snappy.