DEA-C01 Mock Exam 92 — 25 Questions

Question 1

A data team uses the CloudFormation template in the exhibit to create an S3 bucket for storing log files. After one year, they notice that the bucket size is larger than expected. They investigate and find that older versions of objects are not being deleted or transitioned. What is the most likely cause?

Accepted Answer

The lifecycle rule does not apply to noncurrent versions because it lacks NoncurrentVersionExpiration or NoncurrentVersionTransition.. Option A is correct because the lifecycle rule in the CloudFormation template is missing the `NoncurrentVersionExpiration` or `NoncurrentVersionTransition` properties. When S3 bucket versioning is enabled, lifecycle rules that only specify `ExpirationInDays` or `Transition` apply exclusively to the current version of objects. To manage older (noncurrent) versions, you must explicitly include `NoncurrentVersionExpirationInDays` or `NoncurrentVersionTransitionInDays` in the rule. Without these, noncurrent versions accumulate indefinitely, causing the bucket size to grow larger than expected.

Answer

The lifecycle rule is not enabled because the Status property is not set to 'Enabled' properly.

Answer

The bucket has versioning enabled, but the lifecycle rule only applies to current versions.

Answer

The expiration in days is set to 365, which is too short.

Question 2

A company is running a data warehouse on Amazon Redshift. The data engineering team notices that query performance has degraded over time. They suspect that data distribution is causing excessive data movement between nodes. The table is joined frequently on the customer_id column. Which column should be chosen as the distribution key to optimize join performance?

Accepted Answer

customer_id. The correct answer is B (customer_id) because Redshift distributes data across nodes based on the distribution key. When two tables are joined on customer_id, using it as the distribution key ensures that matching rows from both tables are co-located on the same node, eliminating the need for data redistribution (broadcast or shuffle) during the join. This minimizes network traffic and reduces query latency, directly addressing the performance degradation caused by excessive data movement.

Answer

AUTO distribution

Answer

order_date

Answer

EVEN distribution

Question 3

A company is designing a data lake on Amazon S3. The data engineering team needs to implement a lifecycle policy to manage costs. Which TWO actions should be taken to reduce storage costs?

Accepted Answer

Transition objects to S3 Glacier Deep Archive after 90 days.. Option A is correct because transitioning objects to S3 Glacier Deep Archive after 90 days significantly reduces storage costs for data that is rarely accessed and can tolerate a retrieval time of 12 hours. This lifecycle policy is a standard cost-optimization strategy for data lakes where historical or cold data does not require immediate access.

Answer

Transition objects to S3 One Zone-IA after 30 days.

Answer

Enable S3 Intelligent-Tiering.

Answer

Transition objects to S3 Standard-IA after 30 days.

Question 4

A company runs an Amazon RDS for PostgreSQL database for its e-commerce platform. The application team reports that write-intensive workloads are causing high latency and the database is experiencing storage bottlenecks. The database currently uses General Purpose SSD (gp2) storage. Which action would be MOST effective in improving write performance without changing the database instance class?

Accepted Answer

Change the storage type to General Purpose SSD (gp3).. D is correct because gp3 storage provides a baseline performance that is higher than gp2 for the same storage size, and it allows you to independently provision IOPS and throughput without needing to increase storage. This directly addresses the write-intensive workload's high latency and storage bottleneck by offering up to 4,000 IOPS at no additional cost (compared to gp2's 3,000 IOPS baseline for larger volumes), and you can scale IOPS up to 16,000 without changing the instance class.

Answer

Create a read replica and offload writes to it.

Answer

Switch the storage type to Provisioned IOPS SSD (io1).

Answer

Enable Multi-AZ deployment for high availability.

Question 5

A company is using Amazon Redshift for its data warehouse. The data engineering team needs to improve query performance for a large fact table that is frequently joined with multiple dimension tables. Which THREE strategies should be considered?

Accepted Answer

Define sort keys on columns used in WHERE clauses.. Option A is correct because defining sort keys on columns used in WHERE clauses allows Amazon Redshift to use zone maps to skip large blocks of data that do not satisfy the filter condition, dramatically reducing the amount of data scanned. This is especially effective for large fact tables where selective filters can prune entire disk blocks, improving query performance without additional hardware.

Answer

Use DISTSTYLE EVEN to distribute data evenly.

Answer

Increase the number of nodes in the cluster.

Question 6

A data engineer runs the AWS CLI command to retrieve the lifecycle configuration of the 'my-data-lake' bucket. The output is shown in the exhibit. What is the effect of this lifecycle policy?

Accepted Answer

Objects in the 'logs/' prefix are moved to S3 Standard-IA after 30 days, to Glacier after 90 days, and deleted after 365 days.. Option C is correct because the lifecycle policy explicitly applies to the 'logs/' prefix, transitioning objects to S3 Standard-IA after 30 days, then to Glacier after 90 days, and finally expiring (deleting) them after 365 days. The 'Expiration' action with 'Days: 365' permanently removes the objects, while the 'Transitions' define the storage class changes at the specified intervals.

Answer

Objects in the 'logs/' prefix are deleted after 365 days and their delete markers are removed.

Answer

All objects in the bucket are moved to STANDARD_IA after 30 days.

Answer

Objects in the 'logs/' prefix are moved to Glacier after 90 days and expired after 90 days.

Question 7

A company is migrating its on-premises Oracle database to Amazon Aurora PostgreSQL. The migration must have minimal downtime. The source database is 2 TB and runs on a single server. Which AWS service should be used for the migration?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it supports homogeneous migrations from Oracle to Amazon Aurora PostgreSQL with minimal downtime using ongoing replication (change data capture). DMS can handle a 2 TB source database by performing a full load followed by continuous replication of changes from the Oracle redo logs, allowing the target Aurora database to stay nearly in sync until cutover.

Answer

AWS DataSync

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball Edge

Question 8

A company runs a data pipeline using AWS Lambda to process records from an Amazon Kinesis Data Stream. Recently, the Lambda function has been experiencing high invocation errors and the stream is throttling. The function performs simple transformations and writes to Amazon S3. What is the most effective way to reduce throttling and errors?

Accepted Answer

Increase the batch size in the Lambda event source mapping.. Increasing the batch size in the Lambda event source mapping allows each invocation to process more records from the Kinesis stream, reducing the number of total invocations. This lowers the rate at which Lambda polls the stream, which decreases the likelihood of hitting the Kinesis read throughput limits (5 transactions per second per shard) and reduces throttling errors. The simple transformations and S3 writes are likely I/O-bound, so larger batches improve throughput without increasing invocation concurrency.

Answer

Increase the Lambda function timeout.

Answer

Enable provisioned concurrency on the Lambda function.

Answer

Increase the number of shards in the Kinesis stream.

Question 9

A data engineering team is designing a data lake on Amazon S3. They need to store raw data in its original format and transformed data in Parquet. The data is accessed by multiple analytics services, including Amazon Athena and Amazon Redshift Spectrum. Compliance requirements mandate that all data be encrypted at rest with AWS KMS and that the encryption keys be rotated every 90 days. Which S3 bucket configuration meets these requirements?

Accepted Answer

Use SSE-KMS with a customer-managed KMS key that has automatic key rotation enabled.. Option A is correct because SSE-KMS with a customer-managed KMS key allows you to enable automatic key rotation, which meets the 90-day rotation requirement. AWS KMS automatically rotates customer-managed keys annually, but you can also configure a custom rotation period (e.g., 90 days) using a Lambda function or AWS Config rule. This setup ensures raw and Parquet data are encrypted at rest, and the key rotation satisfies compliance mandates.

Answer

Use SSE-C with client-managed keys and rotate them manually.

Answer

Use a bucket policy to enforce encryption and rely on default S3 encryption.

Answer

Use SSE-S3 with default encryption enabled.

Question 10

A company is using Amazon RDS for PostgreSQL with Multi-AZ deployment. The primary instance fails and a failover occurs. After the failover, the application cannot connect to the database. What is the MOST likely cause?

Accepted Answer

The application is using the old primary instance endpoint instead of the RDS CNAME.. After a Multi-AZ failover in Amazon RDS for PostgreSQL, the DNS CNAME record is automatically updated to point to the new primary instance in the standby Availability Zone. If the application is configured with the old primary instance's endpoint (the specific IP or DNS name of the original instance) instead of the RDS CNAME (which remains constant), it will attempt to connect to the failed instance, which is no longer available. This is the most likely cause of connectivity loss because the CNAME is the stable connection point that follows the active primary.

Answer

The database instance is in a 'stopped' state after failover.

Answer

The Multi-AZ failover requires manual intervention to complete.

Answer

The security group for the RDS instance was not updated during failover.

Question 11

A data engineer is troubleshooting an AWS Glue ETL job that reads from an S3 bucket and writes to a DynamoDB table. The job fails with an AccessDeniedException. The IAM role attached to the Glue job has the policy shown in the exhibit. Which TWO additional permissions are required to resolve the issue?

Accepted Answer

iam:PassRole. Option C is correct because the AWS Glue job must pass the IAM role to itself when it runs, which requires the `iam:PassRole` permission. Without this, the job cannot assume the role and will fail with an AccessDeniedException, even if the role has the necessary S3 and DynamoDB permissions.

Answer

kms:Decrypt

Answer

dynamodb:DescribeTable

Answer

s3:ListBucket

Question 12

A company runs an Amazon RDS for PostgreSQL instance that stores financial data. The company requires point-in-time recovery (PITR) with a retention period of 35 days. Additionally, the company needs to create a new database from a specific snapshot every night for testing. Which combination of actions should the data engineer take to meet these requirements?

Accepted Answer

Enable automated backups with a 35-day retention period and create a manual snapshot each night for testing.. Option A is correct because automated backups in Amazon RDS for PostgreSQL support a maximum retention period of 35 days, which satisfies the PITR requirement. Additionally, creating a manual snapshot each night provides a stable, independent copy for testing without interfering with the automated backup schedule or the source database's performance.

Answer

Create a read replica and promote it to a new instance for testing each night.

Answer

Enable Multi-AZ and use the standby instance for testing.

Answer

Disable automated backups to reduce storage costs and take manual snapshots with 35-day retention.

Question 13

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be stored in Amazon S3 in near real-time with minimal overhead. Which THREE steps should the data engineer take to achieve this? (Choose THREE.)

Accepted Answer

Configure a Lambda function to transform data before delivery to S3.. Option A is correct because a Lambda function can be used as a data transformation step within a Kinesis Data Firehose delivery stream. This allows the clickstream data to be transformed (e.g., parsed, enriched, or reformatted) before being delivered to Amazon S3, enabling near real-time storage with minimal operational overhead.

Answer

Use Kinesis Data Analytics to process and store data in S3.

Answer

Enable S3 cross-region replication for the destination bucket.

Question 14

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is JSON and each record is about 2 KB. The delivery stream is configured to buffer incoming data to 5 MB or 60 seconds, whichever comes first. The data engineering team notices that the S3 bucket contains many small files (average 2 MB), which makes subsequent processing inefficient. They need to reduce the number of small files without increasing the latency beyond 5 minutes. Which solution should they implement?

Accepted Answer

Increase the buffer size to 50 MB and the buffer interval to 300 seconds.. Option B is correct because increasing the buffer size to 50 MB and the buffer interval to 300 seconds directly addresses the root cause: the current 5 MB buffer size triggers a flush too frequently, producing many 2 MB files. By raising the buffer size to 50 MB, each flush will contain more data, resulting in larger S3 objects (up to ~50 MB uncompressed), while the 300-second interval ensures latency stays within the 5-minute requirement. This reduces the number of small files without requiring additional services or post-processing.

Answer

Enable compression (GZIP) on the delivery stream.

Answer

Use a Lambda function to merge small files after delivery.

Answer

Decrease the buffer size to 1 MB and the buffer interval to 60 seconds.

Question 15

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources in Parquet format, and the schema evolves over time. Which approach allows querying the data with Amazon Athena while supporting schema evolution?

Accepted Answer

Use AWS Glue Data Catalog with crawlers to automatically update the table schema.. AWS Glue Data Catalog with crawlers automatically infers and updates the table schema as new Parquet files with evolving schemas are ingested into S3. This allows Athena to query the data using the latest schema without manual intervention, making it the ideal solution for schema evolution in a data lake.

Answer

Define Hive-style partitions in Athena and manually update the schema.

Answer

Use S3 Select to query the data directly without a schema.

Answer

Use Amazon Redshift Spectrum with external tables and update the schema manually.

Question 16

Refer to the exhibit. A data engineer notices that the Redshift cluster 'mycluster' does not have automated backups beyond 7 days. However, the compliance team requires a minimum of 35 days of backup retention. What should the engineer do?

Accepted Answer

Modify the cluster's automated snapshot retention period to 35 days.. Option D is correct because Amazon Redshift allows you to modify the automated snapshot retention period for a cluster up to 35 days. The engineer can use the AWS Management Console, CLI, or API to change the `automated_snapshot_retention_period` parameter from the current 7 days to 35 days, meeting the compliance requirement without additional manual intervention.

Answer

Change the node type to ra3.xlplus to enable automatic backups for 35 days.

Answer

Enable audit logging to capture changes for recovery.

Answer

Take manual snapshots every day and retain them for 35 days.

Question 17

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

Accepted Answer

s3:x-amz-server-side-encryption-aws-kms-key-id. Option A is correct because the condition `s3:x-amz-server-side-encryption-aws-kms-key-id` enforces that objects uploaded to S3 must use a specific AWS KMS key for server-side encryption (SSE-KMS). This satisfies the security team's requirement that all data be encrypted at rest using SSE-KMS with AWS KMS managed keys, as it explicitly checks for the presence and value of the KMS key ID in the request.

Answer

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

Answer

s3:x-amz-server-side-encryption

Answer

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

Question 18

A startup is building a data pipeline to ingest user activity logs from a mobile app. The logs are sent in real-time via HTTP POST requests. The data volume is low (a few hundred requests per second) but can spike to a few thousand during promotions. The team wants to store the logs in Amazon S3 for analysis. They also need to be able to query the data using Amazon Athena with minimal latency. The data must be transformed from JSON to Parquet and partitioned by date. The team is considering using Amazon API Gateway with AWS Lambda to receive the logs and write to S3. However, they are concerned about Lambda cold starts and the complexity of handling spikes. Which alternative solution should they choose?

Accepted Answer

Use Amazon Kinesis Data Firehose with a HTTP endpoint as source, enable Parquet conversion, and deliver to S3 with dynamic partitioning. Option A is correct because Kinesis Data Firehose can be used as a HTTP endpoint (via API Gateway or directly with Firehose API), automatically buffers data, converts to Parquet, and writes to S3 with partitioning by date. This handles spikes without custom code. Option B (Lambda + SQS) adds complexity and still faces cold starts. Option C (EMR) is overkill. Option D (Kinesis Data Streams + Lambda) still requires Lambda and has cold start issues.

Answer

Use Amazon API Gateway with AWS Lambda that sends logs to Amazon SQS, then a separate Lambda reads from SQS and writes to S3

Answer

Use Amazon Kinesis Data Streams with AWS Lambda to process and write to S3

Answer

Use Amazon EMR with Spark Streaming to ingest logs from a custom endpoint

Question 19

A company is evaluating Amazon DynamoDB for a new application. The application requires single-digit millisecond latency for read and write operations. Which TWO DynamoDB features should the company enable to achieve this? (Choose TWO.)

Accepted Answer

Use DAX with Write-Through caching.. Option A is correct because DAX with Write-Through caching ensures that every write to DynamoDB is also written to the DAX cache, so subsequent reads of the same item are served from the in-memory cache with single-digit millisecond latency. Option D is correct because DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache that reduces read response times from single-digit milliseconds to microseconds, directly meeting the latency requirement for read operations.

Answer

Enable DynamoDB Global Tables.

Answer

Enable DynamoDB Streams.

Answer

Enable auto-scaling for read and write capacity.

Question 20

A company has an Amazon DynamoDB table with on-demand capacity mode. The table stores session data for a web application. Recently, the application experienced throttling errors during a traffic spike. The team wants to prevent future throttling while optimizing costs. What should they do?

Accepted Answer

Implement a DynamoDB Accelerator (DAX) cluster. A DynamoDB Accelerator (DAX) cluster provides an in-memory cache that absorbs read-heavy traffic spikes, reducing the number of read requests that reach the underlying DynamoDB table. Since the throttling errors occurred during a traffic spike and the table uses on-demand capacity, which already scales automatically for writes and reads, the bottleneck is likely read-heavy traffic overwhelming the table's throughput. DAX offloads reads from the table, preventing throttling without requiring any changes to capacity mode, and it is cost-effective because it reduces read capacity unit consumption.

Answer

Enable DynamoDB auto scaling on the table

Answer

Switch to provisioned capacity with auto scaling

Answer

Increase the read and write capacity of the table

DEA-C01 Mock Exam 92 — 25 Questions

Exhibit

DEA-C01 Mock Exam 92 — 25 Questions

Exhibit