DEA-C01 Practice Test 34 — 15 Questions

Question 1

A company uses Amazon Redshift for its data warehouse. The data engineering team loads data daily from Amazon S3 using COPY commands. Recently, the load performance has degraded because the S3 bucket contains many small files. The team needs to optimize the COPY operation to improve performance. Which approach should they take?

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

Accepted Answer

Use a manifest file that lists only the necessary files, and consolidate small files into larger ones before loading.. The performance degradation is caused by the overhead of processing many small files during the COPY command. Consolidating small files into larger ones (e.g., 100 MB–1 GB each) reduces the number of S3 GET requests and the metadata overhead on Redshift, directly improving load throughput. Using a manifest file further optimizes by explicitly listing only the required files, avoiding unnecessary S3 list operations.

Answer

Use Redshift Spectrum to query the data directly from S3 without loading.

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Enable automatic compression on the Redshift table.

Question 2

A company needs to store archival logs that must be retained for 10 years. The logs are accessed infrequently, but when accessed, retrieval must occur within 12 hours. Which storage class is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is the most cost-effective storage class for archival logs that must be retained for 10 years with infrequent access and a 12-hour retrieval window. It offers the lowest storage cost among S3 classes, with retrieval times typically within 12 hours for standard retrievals, making it ideal for long-term archival data that is rarely accessed.

Answer

Amazon S3 Intelligent-Tiering

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Amazon S3 Standard

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Amazon S3 One Zone-Infrequent Access

Question 3

Arrange the steps to set up cross-region replication for an S3 bucket.

Accepted Answer

Enable versioning on source and destination, then create the destination bucket, then add a replication rule, then assign an IAM role.. First, enable versioning on source and destination. Then create the destination bucket, add a replication rule, and assign an IAM role for replication.

Answer

Create the destination bucket first, then enable versioning on source and destination, then add a replication rule, then assign an IAM role.

Answer

Enable versioning on source and destination, then add a replication rule, then create the destination bucket, then assign an IAM role.

Answer

Enable versioning on source and destination, then create the destination bucket, then assign an IAM role, then add a replication rule.

Question 4

A data engineer is designing a data lake on Amazon S3. The data ingestion pipeline must handle both batch and streaming data. The engineer wants to use a single service to ingest both types of data. Which service should the engineer choose?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service that can ingest both batch and streaming data and deliver it directly to Amazon S3 without requiring custom code. It can receive streaming data from sources like Kinesis Data Streams or Amazon CloudWatch Logs, and also handle batch data via API calls, making it a single ingestion point for both patterns. While AWS Glue does support both batch and streaming ETL, it is primarily an ETL service that requires writing and managing jobs, and is not designed as a direct data ingestion service to S3 without additional configuration. Therefore, Kinesis Data Firehose is the most appropriate service for this requirement.

Answer

Amazon Athena

Answer

S3 Transfer Acceleration

Answer

AWS Glue

Question 5

A data pipeline ingests JSON data from an S3 bucket using AWS Glue. The JSON files contain nested structures, and the team wants to flatten them for analysis in Amazon Athena. Which Glue transformation is most appropriate?

Accepted Answer

Relationalize. Relationalize is specifically designed to flatten nested JSON into relational tables. Option A (Map) applies a function to each record. Option B (Filter) removes records. Option C (Join) combines datasets.

Answer

Filter

Answer

Join

Answer

Map

Question 6

A company is using Amazon Kinesis Data Streams to process real-time stock trade data. The data is consumed by a Lambda function that calculates moving averages and stores results in Amazon DynamoDB. The Lambda function is failing with 'ProvisionedThroughputExceededException' on the DynamoDB table. The table has on-demand capacity. Which TWO actions should the engineer take to resolve this issue?

Accepted Answer

Add a dead-letter queue and configure the Lambda function to retry on failure with exponential backoff.. Adding a dead-letter queue (DLQ) and configuring the Lambda function to retry on failure with exponential backoff allows the function to handle transient ProvisionedThroughputExceededExceptions from DynamoDB. Since the table uses on-demand capacity, the exception indicates a momentary throttle due to traffic spikes; exponential backoff with retries gives DynamoDB time to scale up, while the DLQ captures records that persistently fail for later analysis.

Answer

Decrease the batch window to 0 seconds to process records immediately.

Answer

Increase the Lambda function's reserved concurrency to process more shards.

Question 7

Which THREE factors should a data engineer consider when choosing between Amazon RDS and Amazon DynamoDB for a new application? (Choose three.)

Accepted Answer

Whether the workload requires serverless scaling.. Amazon RDS is a relational database service that requires provisioning and managing server capacity, while DynamoDB is a fully managed NoSQL key-value and document database that supports serverless scaling. Option A is correct because DynamoDB can automatically scale throughput capacity up or down based on traffic patterns, making it suitable for unpredictable workloads, whereas RDS requires manual scaling or the use of Auto Scaling with predefined policies.

Answer

Whether the data must be encrypted at rest by default.

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Whether the application requires VPC isolation.

Question 8

A data engineer is designing a data warehouse using Amazon Redshift. The workload includes complex queries that join large tables. The engineer notices that queries are slow due to disk-based operations. Which configuration change would MOST improve query performance?

Accepted Answer

Choose an appropriate distribution style (e.g., KEY or ALL) for the tables.. Choosing an appropriate distribution style (KEY or ALL) minimizes data movement between nodes during query execution. In Amazon Redshift, disk-based operations often result from large volumes of data being redistributed across the network for joins. By colocating related data on the same slices via KEY distribution or replicating small tables with ALL distribution, you reduce the need for broadcast or redistribution, which directly alleviates disk-based spills and improves query performance.

Answer

Define appropriate sort keys on the large tables.

Answer

Increase the number of slices per node by choosing a different node type.

Answer

Enable compression on all columns.

Question 9

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.. Increasing the number of DPUs allocates more memory and processing capacity to the Glue job, directly addressing the MemoryError caused by data growth from 1 GB to 10 GB. Option A is wrong because changing the worker type to G.2X provides more memory per worker, but it is a more expensive option compared to simply increasing DPUs, which scales horizontally. Option C is wrong because converting CSV to Parquet improves performance and reduces storage but does not add memory to the Glue job; the job still runs with the same DPU allocation. Option D is wrong because migrating to Amazon EMR introduces additional operational complexity and cost; increasing DPUs in Glue is simpler and more cost-effective for scaling an existing job.

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 10

A company is using Amazon S3 for a data lake. The data engineer needs to ensure that all new objects are automatically encrypted with a customer-managed KMS key and that the bucket policy enforces encryption. Which THREE steps should be taken? (Choose THREE.)

Accepted Answer

Configure default encryption on the bucket to use SSE-KMS.. Configuring default encryption on the S3 bucket to use SSE-KMS ensures that any object uploaded without an explicit encryption header is automatically encrypted with the specified customer-managed KMS key. This satisfies the requirement for automatic encryption of all new objects.

Answer

Use a lifecycle policy to apply encryption to existing objects.

Answer

Enable AWS CloudTrail to monitor encryption.

Question 11

Order the steps to set up an Amazon EMR cluster for processing data in S3 using Spark.

Accepted Answer

Prepare the S3 bucket, then launch the EMR cluster with Spark, then configure instances, then submit the job, then terminate the cluster.. First, prepare the S3 bucket. Then launch the EMR cluster with Spark, configure instances, submit the job, and terminate.

Answer

Launch the EMR cluster with Spark, then prepare the S3 bucket, then configure instances, then submit the job, then terminate the cluster.

Answer

Prepare the S3 bucket, then configure instances, then launch the EMR cluster with Spark, then submit the job, then terminate the cluster.

Answer

Submit the job, then launch the EMR cluster with Spark, then configure instances, then prepare the S3 bucket, then terminate the cluster.

Question 12

A data engineer needs to monitor the number of records processed by a Kinesis Data Firehose delivery stream and set an alarm if the count drops below a threshold. Which CloudWatch metric should be used?

Accepted Answer

IncomingRecords. 'IncomingRecords' counts records received by Firehose, which directly indicates processing volume. Option B is wrong because 'IncomingBytes' measures bytes, not records. Option C is wrong because 'DeliveryToS3.Success' is a success metric but measures successful deliveries, not record count. Option D is wrong because 'PutRecord.Success' is a per-API call metric for Kinesis Data Streams, not Firehose.

Answer

PutRecord.Success

Answer

DeliveryToS3.Success

Answer

IncomingBytes

Question 13

A data engineer is using Amazon Athena to query data stored in an S3 bucket. The queries are running slowly. Which THREE actions can improve query performance?

Accepted Answer

Partition the data on commonly filtered columns.. Partitioning data on commonly filtered columns (Option A) improves Athena query performance by reducing the amount of data scanned. Athena uses Hive-style partitioning (e.g., `s3://bucket/table/year=2023/month=01/`), and when a query includes a filter on the partition column, Athena prunes partitions and only reads the relevant S3 prefixes. This directly reduces I/O and query cost, as Athena charges per TB of data scanned.

Answer

Convert the data to JSON format for better schema evolution.

Answer

Move the data to S3 Standard-IA storage class.

Question 14

A company uses Amazon Kinesis Data Streams to ingest IoT sensor data. The data is processed by an AWS Lambda function that transforms the records and writes to an Amazon S3 bucket. Recently, the Lambda function has been failing with 'Rate exceeded' errors for the S3 PUT API calls. The data volume is 10 MB/s with average record size 2 KB. What should be done to resolve this issue?

Accepted Answer

Add a random prefix to the S3 object key to distribute writes across multiple prefixes. The 'Rate exceeded' error for S3 PUT API calls indicates that the Lambda function is hitting S3 request rate limits. S3 buckets have a default limit of 3,500 PUT requests per second per prefix. With a data volume of 10 MB/s and an average record size of 2 KB, the Lambda function is generating approximately 5,000 PUT requests per second (10 MB/s ÷ 2 KB), which exceeds the per-prefix limit. Adding a random prefix to the S3 object key distributes writes across multiple prefixes, effectively increasing the aggregate request rate limit to 3,500 PUT requests per second per prefix, thereby resolving the throttling issue.

Answer

Switch to Amazon Kinesis Data Firehose to write to S3

Answer

Increase the Lambda function's reserved concurrency

Answer

Increase the number of Kinesis shards

Question 15

A company uses AWS Glue DataBrew to clean and transform data for analytics. The source data is in Parquet format in Amazon S3. The transformation includes filtering rows and adding calculated columns. What is the MOST cost-effective way to run these transformations on a schedule?

Accepted Answer

Create a Glue DataBrew recipe and schedule the job using a cron expression. AWS Glue DataBrew is purpose-built for visual data preparation, and scheduling a DataBrew recipe job with a cron expression directly meets the requirement to run filtering and column calculations on Parquet data in S3 without writing code. This is the most cost-effective approach as it avoids provisioning or managing compute resources beyond the serverless DataBrew job runs.

Answer

Use Amazon EMR with Spark

Answer

Create an AWS Lambda function triggered by S3 events

Answer

Use AWS Glue ETL with PySpark