DEA-C01 Practice Test 35 — 15 Questions

Question 1

A data engineer is building a pipeline to ingest data from an on-premises Oracle database into Amazon S3. The pipeline must capture change data (CDC) in near real-time and handle schema changes. Which TWO AWS services should the engineer use?

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

Accepted Answer

AWS Glue Schema Registry. AWS Glue Schema Registry (A) is correct because it enables schema discovery, validation, and evolution for streaming data, allowing the pipeline to handle schema changes from the Oracle CDC source. It integrates with Apache Kafka and Amazon Kinesis Data Streams to enforce schema compatibility rules (e.g., backward, forward, full) as data arrives, ensuring downstream consumers can adapt to evolving schemas without breaking.

Answer

AWS Snowball Edge

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Amazon AppFlow

Answer

Amazon Kinesis Data Streams with Kinesis Agent

Question 2

A company uses AWS Glue to run ETL jobs that transform data from Amazon S3 (Parquet) into a denormalized format for Amazon Redshift. The Glue job uses the DynamicFrame API. The job is failing with a 'MemoryError' when performing a join operation. The data is skewed on the join key. Which THREE actions can reduce memory usage and improve job stability? (Choose THREE.)

Accepted Answer

Use a broadcast join if one of the tables is small enough.. A broadcast join (using `join` with `broadcast` hint or `DynamicFrame.join(..., transformation_ctx='...')` with broadcast enabled) avoids shuffling the larger table across the cluster by copying the small table to every executor. This eliminates the memory pressure from skewed key distribution during the shuffle phase, reducing the risk of a MemoryError.

Answer

Increase the number of DPUs for the Glue job.

Answer

Repartition the data on the join key before the join operation.

Question 3

A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The source files are in a bucket with thousands of small files. What is the best practice to optimize the Glue job performance?

Accepted Answer

Enable 'Group small files' in the Glue job or use a DynamicFrame with coalesce.. Enabling 'Group small files' in AWS Glue automatically coalesces thousands of small input files into larger partitions, reducing the number of tasks and minimizing overhead from task scheduling and S3 list operations. This is the recommended best practice for handling small files in Glue ETL jobs, as it optimizes read performance without requiring manual coalesce or repartitioning.

Answer

Convert the JSON files to CSV before processing with Glue.

Answer

Use an AWS Lambda function to pre-process the files.

Answer

Increase the number of DPUs to the maximum.

Question 4

A company stores sensitive customer data in Amazon S3. The security team requires that all objects be encrypted at rest using server-side encryption with customer-provided keys (SSE-C). Which bucket policy condition will enforce this requirement?

Accepted Answer

s3:x-amz-server-side-encryption-customer-key. The condition key `s3:x-amz-server-side-encryption-customer-key` is specifically used to enforce that objects uploaded to S3 must use server-side encryption with customer-provided keys (SSE-C). This condition key checks for the presence of the `x-amz-server-side-encryption-customer-key` header in the request, which is required for SSE-C encryption. Without this header, the request is denied, ensuring all objects are encrypted at rest using customer-provided keys.

Answer

s3:x-amz-server-side-encryption-aws-kms-key-id

Answer

s3:x-amz-server-side-encryption

Answer

s3:x-amz-server-side-encryption-customer-algorithm

Question 5

A data engineer is designing a data lake on AWS using Amazon S3. The data consists of CSV files generated by IoT devices. The data is accessed by multiple analytics jobs, and the engineer needs to ensure that new files are immediately visible to all consumers after writing. What S3 consistency model applies?

Accepted Answer

Strong consistency for all operations.. Amazon S3 now provides strong consistency for all operations. After a successful write of a new object (or overwrite of an existing object), any subsequent read request immediately receives the latest version of the object, and list operations are also strongly consistent. Therefore, for new CSV files written to S3, the applicable model is strong consistency for all operations.

Answer

Consistent reads require S3 Object Lock.

Answer

Eventual consistency for all operations.

Answer

Read-after-write consistency for new object PUTS.

Question 6

A data pipeline uses AWS Glue to process data from an S3 data lake. The pipeline fails intermittently with a 'ThrottlingException' when writing to a DynamoDB table. What is the MOST likely cause?

Accepted Answer

The DynamoDB table's write capacity is insufficient for the workload.. A ThrottlingException from DynamoDB indicates that the request rate to the table has exceeded the provisioned write capacity. AWS Glue jobs can generate high-throughput writes, and if the DynamoDB table's write capacity units (WCUs) are not sufficient to handle the burst, DynamoDB will throttle the requests. This is the most direct cause of the intermittent failure described.

Answer

The network connection between Glue and DynamoDB is unstable.

Answer

The Glue job's timeout setting is too low.

Answer

The Glue job does not have sufficient IAM permissions to write to DynamoDB.

Question 7

A company is building a data lake on Amazon S3 and wants to ingest data from multiple AWS services (CloudTrail, VPC Flow Logs, and ALB logs). The data should be stored in a central S3 bucket with a common partitioning scheme. Which service can be used to collect and centralize this data with minimal configuration?

Accepted Answer

Configure each source service to deliver logs directly to the central S3 bucket.. CloudTrail, VPC Flow Logs, and ALB logs can each be configured to deliver logs directly to a specified S3 bucket, including a central bucket, with no intermediary service required. This approach minimizes configuration overhead and avoids data movement costs, as each service writes natively to S3 using its own built-in delivery mechanism.

Answer

Use AWS Data Pipeline to copy logs from each source S3 bucket to the central bucket.

Answer

Use AWS Glue to crawl the logs from each source and write to a central S3 bucket.

Answer

Set up Amazon Kinesis Data Firehose to ingest logs from each service and write to S3.

Question 8

A company is building a data lake on Amazon S3 and needs to ingest data from multiple sources. The ingestion must be automated and handle schema changes. Which THREE services can be used together to achieve this? (Choose THREE.)

Accepted Answer

AWS Glue Crawler. AWS Glue Crawler (B) automatically discovers and catalogs schemas from data sources. Amazon Kinesis Data Firehose (C) ingests streaming data into S3. AWS Lambda (E) can transform data on the fly. Together, they automate ingestion and handle schema changes. Amazon Redshift (A) is a data warehouse, not a data lake ingestion service. Amazon EMR (D) is a big data processing framework, not primarily for automated schema change handling.

Answer

Amazon Redshift

Answer

Amazon EMR

Question 9

A company is using Amazon DynamoDB as a data store for a real-time application. The application reads a single item by primary key and occasionally updates it. The data engineer notices high read latency during peak hours. Which TWO actions would most effectively reduce read latency?

Accepted Answer

Increase the read capacity units for the table.. Increasing read capacity units (RCUs) for the DynamoDB table directly allocates more read throughput, reducing throttling and latency during peak hours when read demand exceeds provisioned capacity. This ensures the application can handle the higher read load without experiencing request throttling, which is a primary cause of increased latency.

Answer

Enable DynamoDB global tables.

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Add a local secondary index on the table.

Answer

Disable auto-scaling and set a fixed read capacity.

Question 10

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application is experiencing high latency and checkpoint failures. Which THREE actions should the data engineer take to improve performance and reliability? (Choose three.)

Accepted Answer

Increase the parallelism of the Flink application. Options A, B, and C are correct. Option A: Increasing parallelism improves throughput by distributing workload across more resources. Option B: Using event time processing helps handle out-of-order data and can reduce latency and checkpoint failures by allowing more accurate watermarks. Option C: Increasing the checkpoint interval reduces the frequency of checkpoint operations, which can reduce checkpoint failures under high load. Option D is incorrect because decreasing parallelism reduces throughput, worsening latency. Option E is incorrect because disabling checkpointing removes fault tolerance and does not improve performance.

Answer

Decrease the parallelism to reduce resource contention

Answer

Disable checkpointing to avoid checkpoint failures

Question 11

A data engineer needs to transfer 50 TB of historical data from an on-premises HDFS cluster to Amazon S3. The network bandwidth is limited to 100 Mbps. The transfer must be completed within one week. Which service should be used?

Accepted Answer

AWS Snowball. AWS Snowball is the correct choice because transferring 50 TB over a 100 Mbps network would take approximately 46 days (50 TB * 8 bits/byte / 100 Mbps / 86400 seconds/day), far exceeding the one-week deadline. Snowball provides a physical storage device that can be shipped to the on-premises location, allowing data to be loaded locally and shipped to AWS, bypassing network bandwidth constraints entirely.

Answer

AWS DataSync

Answer

Amazon CloudFront

Answer

AWS Database Migration Service (DMS)

Question 12

A company uses Amazon EMR to process large datasets stored in Amazon S3. The data engineer notices that EMR tasks are failing with 'DiskOutOfSpace' errors. The cluster uses m5.xlarge instances with 1 EBS volume of 64 GB. What is the MOST cost-effective solution to resolve this issue?

Accepted Answer

Increase the EBS storage volume size for each instance and use spot instances for task nodes.. Increasing the EBS volume size provides additional disk space per instance, directly resolving the disk out-of-space error. Using spot instances for task nodes reduces cost. Option A is incorrect because mixing on-demand and spot instances does not increase per-instance disk space. Option C is incorrect because switching to D2 instances is more expensive and may not be necessary. Option D is incorrect because adding more task instances distributes the workload but does not increase the disk space available to each instance, so individual tasks may still fail due to disk space.

Answer

Use a mix of on-demand and spot instances for core nodes.

Answer

Switch to D2 instances which have more instance store volume.

Answer

Increase the number of task instances to distribute the workload.

Question 13

A data engineering team uses Amazon Redshift for analytics. They notice that queries on a large fact table are slow. The table is distributed using DISTSTYLE ALL. Which design change would most likely improve query performance?

Accepted Answer

Change DISTSTYLE to KEY on a column used in frequent joins.. DISTSTYLE ALL copies the entire table to every node, which is inefficient for large fact tables because it wastes storage and network bandwidth during data loading and query execution. Changing to DISTSTYLE KEY on a column used in frequent joins collocates related rows on the same slice, reducing the need to broadcast or redistribute data across the network during joins, which directly improves query performance.

Answer

Change DISTSTYLE to EVEN to distribute rows evenly across slices.

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Change the table to use a SORTKEY on the most frequently filtered column.

Question 14

A data engineer needs to ingest JSON files from an Amazon S3 bucket into an Amazon DynamoDB table. The files are uploaded every hour. Which THREE services can be used together to build this ingestion pipeline?

Accepted Answer

Amazon SQS. Amazon SQS is correct because it decouples the ingestion pipeline, allowing S3 Event Notifications to send messages to an SQS queue when new JSON files arrive. AWS Lambda can then poll the SQS queue to process the files and write to DynamoDB, ensuring reliable, asynchronous ingestion without data loss.

Answer

AWS Step Functions

Answer

Amazon DynamoDB Streams

Question 15

A company runs a MySQL-compatible Amazon Aurora database for its e-commerce platform. The database experiences high write latency during peak hours. The application performs frequent INSERT and UPDATE operations on a table with 50 million rows. The DB instance is db.r5.large with 500 GB of Provisioned IOPS storage. A recent performance analysis shows that the average queue depth is consistently above 32 and write latency exceeds 50 ms. The company needs to reduce write latency without changing the application code. What should a data engineer do?

Accepted Answer

Resize the DB instance to a larger instance type such as db.r5.xlarge.. The high queue depth (consistently above 32) and write latency exceeding 50 ms indicate that the current db.r5.large instance is CPU or I/O constrained for the write workload. Resizing to a larger instance type such as db.r5.xlarge increases the available vCPUs, memory, and network bandwidth, which directly reduces queue depth and write latency by allowing more concurrent write operations to be processed. This solution does not require application code changes and addresses the root cause of insufficient compute capacity for the frequent INSERT and UPDATE operations on the 50-million-row table.

Answer

Enable Aurora Auto Scaling to automatically add reader instances.

Answer

Convert the cluster to Aurora Serverless v2 to automatically scale compute capacity.

Answer

Create a read replica and configure the application to offload read queries.