DEA-C01 Practice Exam 63 — 20 Questions

Question 1

A company is designing a data ingestion pipeline for real-time IoT sensor data. The data volume peaks at 10,000 messages per second. The pipeline must process messages in order per sensor and persist raw data to Amazon S3 for archival. Which TWO services should be used together to meet these requirements? (Choose TWO.)

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

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Streams (KDS) provides the ordered, real-time ingestion layer required for per-sensor message ordering, as it partitions data by a partition key (e.g., sensor ID) and guarantees order within a shard. Amazon Kinesis Data Firehose then reliably reads from the KDS stream and delivers the raw data to Amazon S3 for archival, handling buffering, compression, and automatic retries without requiring custom code.

Answer

AWS Lambda

Answer

Amazon AppFlow

Answer

Amazon Simple Queue Service (Amazon SQS)

Question 2

A company is building a data lake on S3 and needs to ingest data from on-premises Oracle database. The data is 5 TB and changes incrementally. The ingestion must capture changes in near real-time (less than 1 minute latency) and be cost-effective. Which approach should be used?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to S3. AWS DMS with ongoing replication captures incremental changes from Oracle using its native change data capture (CDC) mechanism, such as Oracle LogMiner or binary logs, and streams them to S3 in near real-time with latency under 1 minute. This approach is cost-effective because DMS charges only for the compute resources used during replication, and S3 storage is inexpensive, making it ideal for a 5 TB dataset with continuous changes.

Answer

Use Amazon Kinesis Data Firehose with an Oracle JDBC connector

Answer

Use AWS Glue to perform a full table export daily

Answer

Use AWS DataSync to sync the Oracle data files to S3

Question 3

A data engineer is managing an Amazon Redshift cluster used for analytics. The cluster has a single node of type dc2.large. The engineer notices that queries are slowing down as data volume grows. The cluster's disk space is at 70% usage. The engineer needs to improve query performance and accommodate future growth. The budget allows for moderate cost increase. Which action should the engineer take?

Accepted Answer

Add another dc2.large node to the cluster.. The dc2.large node is a dense compute node with limited storage. Adding another dc2.large node (option A) increases both compute capacity and storage, which directly addresses the slowdown and provides room for growth at a moderate cost. Option B (resizing to a single ds2.xlarge node) increases storage but may not improve compute performance proportionally, as ds2 nodes are dense storage. Option C (single ra3.xlplus with managed storage) separates compute and storage but still has a single compute endpoint, and the cost is higher. Option D (concurrency scaling) adds cost without resolving the single-node bottleneck. Therefore, option A is the best choice.

Answer

Resize the cluster to a single ds2.xlarge node.

Answer

Migrate the cluster to a single ra3.xlplus node with managed storage.

Answer

Enable concurrency scaling and maintain the current cluster.

Question 4

A data engineer is troubleshooting a nightly ETL job that extracts data from an Amazon RDS MySQL instance and loads it into an Amazon S3 bucket in Parquet format. The job runs on an Amazon EMR cluster and has been failing with the error 'Access Denied' when writing to S3. The IAM role attached to the EMR cluster has permissions for S3 PutObject. What is the MOST likely cause?

Accepted Answer

The S3 bucket policy denies access to the EMR cluster's IAM role.. S3 bucket policies can override IAM permissions; if the bucket policy denies access from the EMR cluster, the write will fail even with IAM allow. Option A is wrong because KMS permissions are needed only if the bucket uses SSE-KMS, which is not indicated. Option B is wrong because S3 Lifecycle rules do not affect write permissions. Option C is wrong because EMR cluster termination would cause a different error.

Answer

The S3 bucket uses SSE-KMS encryption and the EMR role lacks kms:GenerateDataKey permission.

Answer

The S3 bucket has a Lifecycle rule that expires objects too quickly.

Answer

The EMR cluster was terminated before the write operation completed.

Question 5

A data engineer notices that an Amazon Redshift cluster is experiencing slow query performance. The engineer suspects that tables are not properly sorted. Which diagnostic query should the engineer run to identify unsorted rows?

Accepted Answer

SELECT * FROM SVV_TABLE_INFO ORDER BY unsorted DESC;. The `SVV_TABLE_INFO` system view in Amazon Redshift provides metadata about each table, including the `unsorted` column which shows the percentage of unsorted rows. By ordering by `unsorted DESC`, the engineer can quickly identify tables with the highest proportion of unsorted data, which directly impacts query performance due to inefficient zone maps and scan pruning.

Answer

SELECT * FROM PG_CATALOG;

Answer

SELECT * FROM STV_TBL_PERM;

Answer

SELECT * FROM STL_LOAD_ERRORS;

Question 6

A company runs an Amazon EMR cluster that processes data from S3 and writes results back to S3. The cluster uses Spot Instances for task nodes. Some tasks are failing due to Spot Instance interruptions. What is the BEST way to handle this without manual intervention?

Accepted Answer

Enable automatic node replacement in the EMR cluster. Amazon EMR's automatic node replacement feature automatically detects when a Spot Instance is interrupted and launches a replacement instance, ensuring the cluster continues processing without manual intervention. Option B is incorrect because manually relaunching the cluster requires human intervention and is not automated. Option C is incorrect because while checkpointing to S3 can help recover data after failures, it does not automatically replace interrupted instances. Option D is incorrect because using only On-Demand instances eliminates cost savings from Spot Instances and does not address the interruption handling problem—it avoids interruptions entirely by not using Spot Instances, but the best approach is to handle interruptions automatically with automatic node replacement.

Answer

Manually relaunch the cluster after failures

Answer

Configure the application to checkpoint to S3 every few minutes

Answer

Use only On-Demand instances for task nodes

Question 7

A company is using Amazon RDS for MySQL and needs to reduce read latency for a global user base. Which AWS feature should be implemented?

Accepted Answer

Read Replicas. Amazon RDS Read Replicas allow you to offload read traffic from the primary DB instance to one or more read-only copies, which can be placed in different AWS Regions to reduce read latency for a global user base. Unlike Multi-AZ, which is designed for high availability, Read Replicas directly address read performance and latency by distributing read queries closer to users. Cross-Region Replication for RDS MySQL is achieved through Read Replicas, making option C the correct choice for reducing read latency globally.

Answer

Multi-AZ deployment

Answer

Aurora Auto Scaling

Answer

Cross-Region Replication

Question 8

A company runs a multi-AZ Amazon RDS for PostgreSQL instance. They need to run a one-time analytical query that will take several hours and consume significant I/O. The query should not impact the primary workload. What should the data engineer do?

Accepted Answer

Create a read replica of the RDS instance and run the query on the replica.. Creating a read replica of the RDS for PostgreSQL instance allows the analytical query to run on a separate database engine without affecting the primary workload. Read replicas in Amazon RDS use asynchronous replication from the source instance, so the replica can handle heavy I/O and long-running queries independently. This ensures the primary instance remains available for the production workload without performance degradation.

Answer

Run the query directly on the primary instance during off-peak hours.

Answer

Increase the instance size to handle the load.

Answer

Enable Multi-AZ and run the query on the standby instance.

Question 9

A company uses AWS Glue to catalog and transform data in Amazon S3. The Glue ETL jobs are failing intermittently with 'ThrottlingException' errors. Which THREE actions can help mitigate this issue? (Select THREE.)

Accepted Answer

Implement exponential backoff and retry in the Glue job code.. Options A, C, and D are correct. Implementing exponential backoff and retry (A) handles transient ThrottlingException errors by pacing retries. Requesting a service quota increase for the Glue API (C) raises the maximum allowed requests per second. Enabling job bookmarking (D) reduces the amount of data reprocessed, thereby lowering the number of API calls made to Glue. Option B (increasing DPUs) improves job performance but does not directly address API throttling. Option E (switching to Spark SQL) does not change the underlying API call frequency.

Answer

Increase the number of DPUs for the Glue job.

Answer

Switch from PySpark to Spark SQL.

Question 10

A media company stores video files in Amazon S3 buckets organized by content type. The company has a requirement to automatically archive files that are older than 90 days to Amazon S3 Glacier Deep Archive to reduce costs. However, the company wants to retain the ability to restore files within 12 hours if needed. The data engineer creates an S3 Lifecycle policy to transition objects to Glacier Deep Archive after 90 days. After deploying the policy, the engineer notices that the storage costs have not decreased significantly. On reviewing the bucket metrics, the engineer sees that many objects are being deleted directly by users before the lifecycle policy takes effect. The company needs to enforce the lifecycle policy and prevent premature deletions. What should the data engineer do to enforce the lifecycle policy?

Accepted Answer

Enable S3 Versioning on the bucket and add a lifecycle rule to transition noncurrent versions to Glacier Deep Archive after 90 days.. Enabling S3 Versioning ensures that when users delete objects, a delete marker is created instead of permanently deleting the object. The lifecycle rule can then transition the noncurrent versions (the original objects) to Glacier Deep Archive after 90 days, preserving the data while reducing costs. Option A is incorrect because Object Lock with GOVERNANCE mode prevents any deletion or overwrite for the retention period, which may interfere with normal operations and does not address the lifecycle policy enforcement. Option C is incorrect because MFA Delete provides additional security for delete operations but does not prevent the deletion itself; it only requires MFA authentication. Option D is incorrect because S3 Glacier Select is used to query archived data, not for lifecycle management.

Answer

Apply an S3 Object Lock retention policy with a default retention mode of GOVERNANCE and a retention period of 90 days.

Answer

Enable S3 Multi-Factor Authentication (MFA) Delete on the bucket to require MFA for all delete operations.

Answer

Use S3 Glacier Select to query the objects and restore them if needed.

Question 11

Refer to the exhibit. A data engineer is using a Kinesis Data Stream with 2 shards. The producer uses a partition key that is the user ID (a UUID). The consumer is falling behind. Which change would improve throughput?

Accepted Answer

Increase the number of shards. The consumer is falling behind because the total throughput of the stream (1 MB/s or 1,000 records/s per shard for writes, and 2 MB/s per shard for reads) is insufficient for the incoming data volume. Increasing the number of shards scales both the write and read capacity linearly, allowing the consumer to process records faster and catch up. Changing the partition key or retention period does not increase throughput, and switching to Firehose changes the delivery model but does not inherently solve the consumer lag.

Answer

Switch to Kinesis Data Firehose

Answer

Increase the retention period

Answer

Change the partition key to a constant value

Question 12

A healthcare company uses Amazon RDS for PostgreSQL to store patient records. The database has a size of 1 TB and is running on a db.r5.large instance. The company requires that the database be highly available and have automated backups with point-in-time recovery (PITR) for the last 35 days. The operations team has configured Multi-AZ deployment and automated backups with a 35-day retention period. During a recent disaster simulation, the team attempted to restore the database to a point in time from 30 days ago. The restore operation failed because the backup was not available. On investigation, the team found that the automated backups were being deleted before the retention period ended. The team also noticed that the database has a large number of transaction logs generating a high volume of write activity. What is the most likely cause of the backups being deleted prematurely?

Accepted Answer

The RDS instance was deleted, which automatically deletes all automated backups.. When an RDS instance is deleted, all automated backups associated with that instance are automatically deleted, regardless of the configured retention period. In this scenario, the instance was not explicitly described as deleted, but the deletion of automated backups before the retention period ends is a classic symptom of instance deletion. Option B is incorrect because a conflicting backup window does not cause backups to be deleted; it only affects when backups are taken. Option C is incorrect because Multi-AZ deployment does not affect backup retention; backups are still retained according to the retention period even if Multi-AZ is not enabled. Option D is incorrect because manual snapshots are independent of automated backups; deleting manual snapshots does not affect automated backup retention.

Answer

The automated backup window was set to a time that conflicted with the database maintenance window.

Answer

The Multi-AZ deployment was not enabled during the backup process, causing backups to fail.

Answer

The database had manual snapshots that were deleted manually by the operations team.

Question 13

A data engineer created the IAM policy shown in the exhibit. The engineer then attempts to upload an object to 'my-bucket' using the AWS CLI with the command: aws s3 cp file.txt s3://my-bucket/ --sse aws:kms. The upload fails with an 'AccessDenied' error. What is the most likely cause?

Accepted Answer

The policy requires SSE-S3 (AES256), but the command uses SSE-KMS. The IAM policy in the exhibit requires the `s3:x-amz-server-side-encryption` header to be set to `AES256`, which corresponds to SSE-S3. The AWS CLI command uses `--sse aws:kms`, which sets the header to `aws:kms` for SSE-KMS. This mismatch causes the request to fail the `s3:PutObject` condition check in the policy, resulting in an 'AccessDenied' error.

Answer

The policy resource is incorrect

Answer

The policy does not allow the s3:PutObject action

Answer

The command is missing the --sse-customer-algorithm parameter

Question 14

A company uses AWS Lake Formation to manage data lake permissions. A data engineer needs to grant a group of analysts SELECT permission on a set of tables in the 'analytics' database, but only for columns that are not classified as 'PII'. Which approach should the engineer use?

Accepted Answer

Use Lake Formation tag-based access control (LF-TBAC) to grant SELECT on columns without the 'PII' tag.. Lake Formation tag-based access control (LF-TBAC) allows granting SELECT permission on columns that do not have the 'PII' tag, enabling fine-grained column-level access. Option A is incorrect because granting SELECT on the entire database would include all columns, including those classified as PII. Option B is incorrect because IAM policies cannot enforce column-level restrictions based on tags within Lake Formation tables. Option C is incorrect because S3 bucket policies operate at the object level and cannot restrict access to specific columns within a table.

Answer

Grant SELECT on the entire database and rely on analysts to avoid PII columns.

Answer

Create an IAM policy that denies access to PII columns.

Answer

Use an S3 bucket policy to restrict access to objects containing PII data.

Question 15

A company runs an e-commerce platform that generates clickstream data from user interactions on their website. The data is sent as JSON objects via HTTP POST to an API Gateway endpoint, which triggers a Lambda function that writes each record to a Kinesis Data Stream (100 shards). A second Lambda function consumes the stream, transforms the data (enriches with geolocation from a DynamoDB table), and writes to a Kinesis Data Firehose delivery stream that delivers Parquet files to an S3 data lake every 5 minutes. The system has been working for months, but recently the Firehose delivery stream started showing 'DeliveryFailed' errors for a subset of records. The errors point to 'InvalidData' from the Lambda transformation. The engineer reviews the Lambda transformation code and notices that the geolocation lookup occasionally fails because the DynamoDB table has a throttling issue. The engineer needs to handle these failures gracefully so that records that fail enrichment are still delivered to S3 with a null geolocation field, without blocking other records. Which course of action should the engineer take?

Accepted Answer

Modify the Lambda function to catch exceptions during the geolocation lookup, set the geolocation field to null, and continue processing the record.. It modifies the Lambda function to catch exceptions during the geolocation lookup, set the geolocation field to null, and continue processing. This ensures that records that fail enrichment are still delivered to S3 with a null geolocation field, without blocking other records, and without requiring additional infrastructure. Option A is incorrect because Kinesis Data Firehose does not natively support a dead-letter queue (DLQ); failed records can be sent to an S3 bucket for failed data, but that would not include the transformed data with null geolocation. Option B is incorrect because sending failed records to a separate Kinesis Data Stream adds complexity and does not ensure they are delivered to S3 with the desired null geolocation field. Option D is incorrect because increasing RCUs may reduce throttling but does not eliminate the possibility of failures, and it increases cost; the requirement is to handle failures gracefully, not prevent them entirely.

Answer

Configure the Kinesis Data Firehose delivery stream to send failed records to a dead-letter queue (DLQ) for later reprocessing.

Answer

Modify the Lambda function to send failed records to a separate Kinesis Data Stream for manual processing.

Answer

Increase the read capacity units (RCUs) on the DynamoDB table to eliminate throttling.

Question 16

A social media company ingests user activity data from multiple sources using Amazon Kinesis Data Firehose. The data is delivered to Amazon S3 in near-real-time. The company wants to transform the data by adding a timestamp and masking email addresses before storing it in S3. The transformation should be applied to all records. What is the most cost-effective way to implement this transformation?

Accepted Answer

Configure the Firehose delivery stream to invoke a Lambda function for data transformation.. The most cost-effective way is to configure the Kinesis Data Firehose delivery stream to invoke an AWS Lambda function for data transformation before the data is delivered to S3. This approach is serverless and only runs when data is flowing, so you pay only for the compute time used during transformation. Option A (Athena CTAS) would require querying after storage, adding cost and latency. Option B (Glue batch job) runs on a schedule regardless of data volume, leading to idle costs, and introduces latency. Option C (S3 Events with Lambda) triggers after data is already stored, meaning the data is first stored in raw form, then transformed, doubling storage costs and adding complexity. Therefore, the Firehose-integrated Lambda is the most cost-effective and low-latency option.

Answer

Use Amazon Athena to run a CTAS query that transforms the data and writes to a new location.

Answer

Use AWS Glue to schedule a batch job every 5 minutes to transform the data.

Answer

Use Amazon S3 Events to trigger a Lambda function whenever a new object is created.

Question 17

An e-commerce company uses AWS Glue to process clickstream data from its website. The data is stored in Amazon S3 in partitioned Parquet format by date and hour. A recent increase in traffic has caused the Glue job to fail with 'Java heap space' errors. The job runs with 10 DPUs and uses Spark's default configurations. The data engineer needs to resolve the memory issue without modifying the ETL script. What should the data engineer do?

Accepted Answer

Increase the Spark configuration 'spark.sql.shuffle.partitions' to 500.. Increasing 'spark.sql.shuffle.partitions' to 500 reduces the amount of data handled per partition during shuffle operations, which alleviates memory pressure and prevents 'Java heap space' errors. This is a configuration change that does not require modifying the ETL script. Option A is wrong because decreasing partitions increases data per partition, worsening memory issues. Option B is wrong because changing worker type to G.1X (which has more memory per executor) might help but does not address the root cause if the issue is due to too few shuffle partitions; it is also not a direct fix for shuffle memory. Option D is wrong because increasing DPUs adds more executors but does not solve the per-executor memory issue caused by large shuffle partitions.

Answer

Decrease the Spark configuration 'spark.sql.shuffle.partitions' to 50.

Answer

Change the worker type to G.1X.

Answer

Increase the number of DPUs to 20.

Question 18

A data engineer reviewed the S3 lifecycle policy shown in the exhibit. The engineer notices that objects under the 'logs/' prefix are being deleted after 365 days. The business requirement is to retain logs for at least 5 years. What should the engineer change in the lifecycle policy?

Accepted Answer

Set the expiration days to 1825. The business requirement is to retain logs for at least 5 years, which is 1,825 days (5 × 365). The current lifecycle policy sets expiration to 365 days, causing premature deletion. By setting the expiration days to 1,825, the S3 lifecycle policy will delete objects under the 'logs/' prefix only after 5 years, meeting the retention requirement.

Answer

Change the prefix to 'logs/archive/'

Answer

Change the transition to GLACIER on day 365

Answer

Remove the expiration action

Question 19

A company runs a data pipeline using AWS Glue ETL jobs to process daily files from an S3 bucket. The files are in CSV format and range from 1 GB to 10 GB. The Glue job runs successfully for small files but fails with an 'Out of Memory' error for files larger than 5 GB. The job uses a single G.1X DPU (16 GB memory). The company needs to process these large files without changing the existing ETL script. Which solution should the company implement?

Accepted Answer

Increase the number of DPUs and use the G.2X worker type to provide more memory per worker.. Increasing the number of DPUs and switching to the G.2X worker type allocates more memory per worker (32 GB instead of 16 GB), allowing the Glue job to process larger CSV files without modifying the ETL script. Option A is incorrect because converting to Parquet would require changing the script and may still encounter memory limits with very large files. Option B is incorrect because Optimus format is not a standard AWS Glue feature; the correct approach is to increase memory. Option C is incorrect because moving to Amazon EMR with Spark would likely require rewriting the script, which the company wants to avoid.

Answer

Convert the input files from CSV to Parquet format to reduce memory usage.

Answer

Use the Optimus format in AWS Glue to compress data.

Answer

Use Amazon EMR with Spark instead of AWS Glue.

Question 20

A financial services company processes real-time stock trade data. They use Amazon Kinesis Data Streams with a shard count of 5, each shard receiving about 500 records per second. The consumer application uses the Kinesis Client Library (KCL) with DynamoDB for checkpointing. Lately, some records are being processed multiple times. What is the most likely cause?

Accepted Answer

The consumer application is crashing and restarting, causing re-processing of records.. The Kinesis Client Library (KCL) uses DynamoDB to track checkpoint progress for each shard. If the consumer application crashes and restarts, the KCL will resume processing from the last committed checkpoint, which may be behind the actual processing point. This causes records that were already processed (but not yet checkpointed) to be re-processed, leading to duplicate processing.

Answer

The Kinesis stream's iterator age is exceeding the retention period.

Answer

The DynamoDB table used for checkpointing is throttling write requests.

Answer

The record size exceeds the 1 MB API limit, causing retries.