DEA-C01 Practice Exam 51 — 20 Questions

Question 1

A retail company uses AWS Glue ETL jobs to process sales data from an S3 data lake. The source data is partitioned by year/month/day in CSV format. The Glue job reads the latest day's data, performs transformations (e.g., cleaning, aggregating), and writes the results to a separate S3 bucket. The job runs on a schedule every day at 2 AM. Recently, the job has been failing intermittently with the error 'AnalysisException: Path does not exist: s3://source-bucket/year=2024/month=02/day=30/'. The engineer verifies that the folder 'day=30' does not exist because February has only 28 days in 2024. The job is reading data from a hardcoded path. The company expects the job to handle variable days per month automatically. What should the engineer do to fix the issue?

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

Accepted Answer

Use a Glue crawler to populate the Data Catalog and use dynamic frame from_catalog with partition predicates. Using a Glue crawler to populate the Data Catalog and then using dynamic frame with from_catalog allows Glue to automatically discover all existing partitions. This eliminates the need for hardcoded paths and handles variable days per month. Option A (Spark SQL with manual partition pruning) still requires manual handling of partitions. Option B (try-catch) is a workaround but does not fix the root cause. Option C (increasing retries) does not address the missing partition issue.

Answer

Modify the script to use Spark SQL with manual partition pruning based on current date

Answer

Add a try-catch block in the script to skip missing partitions

Answer

Increase the job's retry count and set a timeout

Question 2

A small startup is building a data pipeline to ingest customer orders from a web application into Amazon Redshift for analytics. The orders are written to an Amazon RDS MySQL database. The startup wants to replicate the orders to Redshift in near-real time (within 5 minutes) with minimal operational overhead. The data volume is low, averaging 100 new orders per minute. The startup has a single data engineer who is also responsible for other tasks. What is the simplest solution?

Accepted Answer

Use AWS Database Migration Service (DMS) with continuous replication. AWS DMS can continuously replicate from MySQL to Redshift with minimal setup and low overhead. Option A (AWS Glue) is batch-oriented and may not meet the 5-minute latency. Option B (Amazon EMR) is overkill for low data volumes. Option C (AWS Lambda) requires custom code and may not efficiently handle the replication.

Answer

Use AWS Glue with a scheduled job every 5 minutes to copy data from MySQL to Redshift

Answer

Use Amazon EMR with Spark streaming to read from MySQL and write to Redshift

Answer

Use an AWS Lambda function to query MySQL every minute and insert into Redshift

Question 3

A company is building a data lake on Amazon S3. The data sources include relational databases, streaming data, and log files. The data engineer needs to ensure that the data ingestion pipeline can handle schema evolution, support both batch and streaming, and provide a unified metadata catalog. Which THREE services should the engineer use? (Choose three.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a unified metadata catalog (the AWS Glue Data Catalog) that stores schema information for data stored in Amazon S3. It supports schema evolution by allowing you to update the catalog schema as data formats change, and it integrates with both batch (AWS Glue ETL jobs) and streaming (AWS Glue Streaming ETL) ingestion pipelines, making it the central service for metadata management in a data lake.

Answer

Amazon DynamoDB

Answer

Amazon Athena

Question 4

A company uses Amazon DynamoDB for a gaming application. The application experiences throttling during peak hours. The table's read and write capacity is provisioned. Which TWO actions can reduce throttling?

Accepted Answer

Enable DynamoDB auto scaling for the table. DynamoDB auto scaling (Option B) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, preventing throttling during peak hours without manual intervention. This is the correct action because it dynamically increases capacity when demand spikes and reduces it during low traffic, directly addressing the throttling issue.

Answer

Enable TTL (time to live) on the table to automatically delete old items

Answer

Increase the provisioned read capacity units (RCUs)

Answer

Add a DynamoDB Global Table for the table

Question 5

A company uses Amazon RDS for MySQL as a source for AWS DMS to replicate data to S3. The replication task is failing with 'OutOfMemory' errors on the DMS instance. The source table has 10 million rows with large BLOB columns. Which THREE changes would most likely resolve the issue?

Accepted Answer

Set the LOB column settings to 'Limited LOB mode' and specify a max LOB size.. Setting LOB columns to 'Limited LOB mode' with a specified max LOB size prevents DMS from loading entire LOBs into memory. Instead, DMS truncates LOBs to the specified size, reducing memory consumption and avoiding OutOfMemory errors when replicating large BLOB columns from MySQL to S3.

Answer

Disable logging for the DMS task to free memory.

Answer

Enable Full LOB mode to handle LOBs more efficiently.

Question 6

A data engineer notices that an Amazon Kinesis Data Firehose delivery stream is failing to deliver data to an Amazon S3 bucket. The CloudWatch metrics show 'DeliveryToS3.Success' is 0 and 'S3.BucketExists' is 1. What is the MOST likely cause?

Accepted Answer

The IAM role for Firehose lacks s3:PutObject permission.. The metric 'S3.BucketExists' is 1, confirming the S3 bucket exists, so the issue is not bucket existence. With 'DeliveryToS3.Success' at 0, the failure is in the write operation. The IAM role assumed by Firehose must have the s3:PutObject permission to deliver data; lacking it would cause all delivery attempts to fail silently, matching the observed metrics.

Answer

The S3 bucket has an ACL that denies access to Firehose.

Answer

The Firehose delivery stream Lambda transformation function is failing.

Answer

The S3 bucket does not exist.

Question 7

A data engineer is troubleshooting an Amazon Redshift cluster that is not allowing connections from a specific IP range. The engineer verified that the cluster's security group allows inbound traffic from the IP range. What is the next step to resolve the issue?

Accepted Answer

Check the network ACL (NACL) associated with the Redshift cluster's subnet.. Even if the security group allows inbound traffic from a specific IP range, the network ACL (NACL) associated with the Redshift cluster's subnet can block traffic at the subnet level. NACLs are stateless and can override security group rules. Option A is incorrect because modifying the cluster parameter group does not control network-level access; public accessibility is a separate setting. Option B is incorrect because the engineer already verified the security group, but even if it is correctly attached, the NACL could still block traffic. Option C is incorrect because IAM roles control authentication and authorization, not network connectivity.

Answer

Modify the Redshift cluster parameter group to enable public accessibility.

Answer

Verify that the cluster's security group is attached to the Redshift cluster.

Answer

Check the IAM role associated with the Redshift cluster.

Question 8

A data engineer notices that an Amazon Redshift cluster is running low on disk space. The cluster has three nodes of type dc2.large. Which action will increase the available storage capacity?

Accepted Answer

Increase the number of nodes in the cluster.. Amazon Redshift stores data on the local instance store volumes attached to each node in the cluster. With a dc2.large cluster, each node provides approximately 160 GB of SSD storage. Adding nodes increases the total available storage linearly because each new node contributes its local storage to the cluster. Therefore, increasing the number of nodes is the correct way to expand disk capacity.

Answer

Mount an Amazon S3 bucket as a file system to store data.

Answer

Change the volume type to Provisioned IOPS SSD (io1) to increase capacity.

Answer

Enable automatic compression on the tables.

Question 9

A data engineer needs to transform CSV files to Parquet format using AWS Glue. The source data contains sensitive columns that must be masked. Which Glue feature should be used?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is a visual data preparation tool that includes built-in transformations for masking sensitive data, such as hashing, tokenizing, or redacting columns. This makes it the correct choice for transforming CSV files to Parquet while applying column-level masking without writing custom code.

Answer

AWS Glue Studio

Answer

AWS Glue Crawler

Answer

AWS Glue Schema Registry

Question 10

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda function that writes to an S3 bucket. Recently, the Lambda function started timing out. Which step should be taken to resolve this issue?

Accepted Answer

Increase the Lambda function timeout. The Lambda function is timing out, indicating that the current timeout is insufficient for processing the records from Kinesis within the allowed time. Increasing the timeout gives the function more time to complete its work, resolving the timeout issue. Option B is incorrect because setting reserved concurrency to 1 limits the number of concurrent executions but does not increase the time available per invocation. Option C is incorrect because decreasing the batch size reduces the number of records per invocation but does not extend the timeout; it may even increase the number of invocations. Option D is incorrect because increasing the number of shards increases the stream's throughput and may increase the number of concurrent Lambda invocations, but it does not address the root cause of the function timing out.

Answer

Set the Lambda reserved concurrency to 1

Answer

Decrease the batch size in the event source mapping

Answer

Increase the number of shards in the Kinesis stream

Question 11

Match each AWS networking concept to its definition.

Accepted Answer

VPC: A virtual network dedicated to your AWS account.. Networking fundamentals for AWS: VPC is a virtual network, subnets are subdivisions, security groups are instance-level firewalls, and NACLs are subnet-level stateless firewalls.

Answer

Security Group: A stateless firewall that controls traffic at the subnet level.

Answer

NACL: A virtual firewall that controls traffic at the instance level.

Question 12

A company wants to ingest streaming data from IoT devices into Amazon S3 using Amazon Kinesis Data Firehose. The data must be transformed from JSON to Parquet format before landing in S3. What is the SIMPLEST way to achieve this?

Accepted Answer

Configure Kinesis Data Firehose with a built-in Parquet converter.. Amazon Kinesis Data Firehose has a built-in Parquet conversion feature that uses an AWS Glue schema to convert incoming JSON data to Parquet format. This is the simplest approach because it requires no custom code or additional services; you only need to provide a schema and enable the conversion in the Firehose delivery stream configuration. Option B (using Lambda) is more complex, as it requires writing and maintaining a custom transformation function.

Answer

Use an AWS Lambda function as a data transformation in Kinesis Data Firehose to convert JSON to Parquet.

Answer

Use Kinesis Data Firehose to deliver data directly to S3 in JSON format and run a nightly Glue job to convert to Parquet.

Answer

Use Kinesis Data Analytics to convert the data to Parquet before sending to Firehose.

Question 13

A company uses Amazon RDS for MySQL with encryption at rest enabled. The security team requires that all database audit logs be stored in Amazon S3 for at least 7 years. Which AWS service should the data engineer use to collect and store the logs?

Accepted Answer

Amazon CloudWatch Logs with a subscription filter to Amazon S3.. Amazon RDS for MySQL can publish audit logs to Amazon CloudWatch Logs. A subscription filter can then forward these logs to Amazon S3 for long-term storage, satisfying the 7-year retention requirement. Option A is wrong because S3 Object Lock is a storage feature, not a log collection service. Option B is wrong because Kinesis Data Firehose is not natively integrated with RDS audit logs. Option D is wrong because AWS CloudTrail captures API calls, not database audit logs.

Answer

Amazon S3 with S3 Object Lock enabled for write-once-read-many (WORM) protection.

Answer

Amazon Kinesis Data Firehose to stream logs directly to Amazon S3.

Answer

AWS CloudTrail to capture database queries and store in S3.

Question 14

A data engineer is troubleshooting a slow Amazon Redshift query. The query scans a large table with interleaved sort keys. The engineer notices that the query plan shows a sequential scan instead of a range-restricted scan. What is the MOST likely reason?

Accepted Answer

The table has not been vacuumed and reindexed after large data loads.. Interleaved sort keys require periodic VACUUM REINDEX to maintain sort order. Without it, Redshift may fall back to sequential scans. Option B is incorrect because DISTKEY affects data distribution, not sort key usage. Option C is incorrect because compression does not prevent range-restricted scans. Option D is incorrect because WLM queue slots affect concurrency, not scan type.

Answer

The table has a poor distribution key (DISTKEY) causing data skew.

Answer

The table uses compression encodings that prevent range-restricted scans.

Answer

The workload management (WLM) queue is configured with too few query slots.

Question 15

A company is migrating its data warehouse from on-premises to Amazon Redshift. The migration involves copying 50 TB of data from an S3 bucket to Redshift. The network bandwidth is limited to 1 Gbps. Which TWO approaches should the team use to complete the transfer within 7 days?

Accepted Answer

Use Amazon S3 Transfer Acceleration. Amazon S3 Transfer Acceleration (option A) uses AWS edge locations to accelerate uploads over the public internet by routing traffic through optimized paths, which can significantly improve transfer speeds for large datasets when bandwidth is limited. With 1 Gbps bandwidth, the theoretical maximum transfer for 50 TB over 7 days is approximately 75.6 TB (1 Gbps * 7 days * 86400 seconds/day / 8 bits/byte), so the raw bandwidth is sufficient, but Transfer Acceleration helps overcome latency and packet loss issues that can reduce effective throughput. This makes it a valid approach to ensure the transfer completes within the time window.

Answer

Use AWS Direct Connect with 10 Gbps

Answer

Use AWS Lambda to copy data in parallel

Answer

Use Amazon Kinesis Data Firehose

Question 16

A company uses Amazon S3 to store log files from multiple sources. The logs are partitioned by year, month, day, and hour. A data engineer uses Amazon Athena to query the logs. Recently, users have reported that queries are taking longer than expected. The engineer notices that many queries are scanning large amounts of data even when filtering on partition columns. The total data size is 10 TB, and the average query scans 2 TB. The partition columns are properly defined in the table schema. What is the most likely cause of the slow queries?

Accepted Answer

The table is not partitioned, or the partitions are not properly defined in the table DDL.. The most likely cause. Although the partition columns are defined in the table schema, partition pruning in Athena only works if the data is stored in a matching Hive-style partition layout (e.g., s3://bucket/year=2024/month=01/day=01/hour=12/). If the S3 prefix does not follow this pattern, Athena must scan all partitions, resulting in large data scans. Option A is incorrect because a large number of partitions can cause slow metadata operations but does not inherently increase the amount of data scanned; partition pruning still applies. Option C is incorrect because compressed files reduce the amount of data scanned, not increase it. Option D is incorrect because while columnar formats like Parquet improve query performance by reading only necessary columns, they do not affect partition pruning; the issue here is that partitions are not being pruned.

Answer

The number of partitions is too large, causing Athena to spend time listing partitions.

Answer

The log files are stored in compressed format (e.g., gzip), which increases the amount of data scanned.

Answer

The log files are stored in CSV format instead of columnar formats like Parquet.

Question 17

A financial services company is designing a data store for transaction records that must be immutable and auditable. The data must be stored for 7 years. Which AWS services can be combined to meet these requirements? (Choose TWO.)

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is correct because it provides the lowest-cost storage for long-term retention of immutable data, with a 7-year lifecycle meeting compliance requirements. Amazon S3 with Object Lock enabled is correct because it enforces a write-once-read-many (WORM) model, preventing records from being deleted or overwritten for a specified retention period, ensuring immutability and auditability.

Answer

Amazon EBS volume with snapshots

Answer

Amazon RDS with automated backups

Answer

Amazon DynamoDB with point-in-time recovery

Question 18

A company uses AWS Lake Formation to manage access to data in a data lake. The data engineer needs to grant a user the ability to query tables in the 'sales' database using Amazon Athena, but only when the user's IP address is within the corporate network (10.0.0.0/8). Which combination of actions should the data engineer take?

Accepted Answer

Grant Lake Formation permissions on the tables and attach an IAM policy to the user with aws:SourceIp condition. Lake Formation permissions are needed to grant query access to the tables in the 'sales' database, and an IAM policy attached to the user with a condition key `aws:SourceIp` restricts Athena access to the corporate IP range (10.0.0.0/8). Option A is incorrect because S3 bucket policies with `aws:SourceIp` cannot be used to restrict Athena queries through Lake Formation; Lake Formation manages access at a higher level. Option C is incorrect because using an S3 VPC endpoint alone does not enforce IP-based restrictions; it only restricts network traffic to the VPC. Option D is incorrect because network ACLs operate at the subnet level and do not control access to specific Lake Formation resources or Athena queries.

Answer

Grant Lake Formation permissions on the tables and attach an S3 bucket policy with aws:SourceIp condition

Answer

Use an S3 VPC endpoint and grant Lake Formation permissions on the tables

Answer

Grant Lake Formation permissions on the tables and configure a network ACL in the VPC

Question 19

A company needs to ingest data from an on-premises database to Amazon S3 with minimal impact on the source database. The data volume is several TB. Which AWS service is best suited for this task?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is best suited because it can continuously replicate data from an on-premises database to Amazon S3 with minimal impact on the source. DMS uses change data capture (CDC) to capture only incremental changes after an initial full load, avoiding heavy read loads on the source database. This makes it ideal for migrating several TB of data while keeping the source operational.

Answer

AWS Direct Connect

Answer

AWS Snowball Edge

Answer

Amazon S3 Transfer Acceleration

Question 20

A company is using Amazon MSK (Managed Streaming for Apache Kafka) to ingest real-time data. They need to transform the data using custom Java code before writing to Amazon S3. The transformation must be fault-tolerant and exactly-once semantics are required. Which AWS service should be used?

Accepted Answer

Kafka Connect with S3 Sink Connector. Kafka Connect with S3 Sink Connector supports exactly-once semantics and custom transformations via Single Message Transforms (SMTs) or custom connectors. Option A (Amazon EMR with Spark Streaming) is for batch processing, not real-time exactly-once from MSK. Option B (AWS Lambda) does not provide exactly-once semantics from MSK. Option D (Kinesis Data Analytics for Apache Flink) is for Flink, not Kafka.

Answer

Amazon EMR with Spark Streaming

Answer

AWS Lambda consumer for MSK

Answer

Kinesis Data Analytics for Apache Flink