DEA-C01 Mock Exam 90 — 25 Questions

Question 1

A data engineer runs the describe-stream command and sees the output above. The stream has a retention period of 24 hours. The engineer needs to ensure that consumers can replay data for up to 7 days. Which action is required?

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

Accepted Answer

Use the IncreaseStreamRetentionPeriod API to set retention to 168 hours.. The describe-stream output shows a retention period of 24 hours, but the requirement is to allow consumers to replay data for up to 7 days (168 hours). Amazon Kinesis Data Streams supports modifying the retention period dynamically without recreating the stream, using the IncreaseStreamRetentionPeriod API or the update-shard-count command. Option C correctly uses this API to set retention to 168 hours, which is the maximum supported retention period for Kinesis Data Streams.

Answer

Increase the number of shards to allow more data storage.

Answer

Delete the stream and recreate it with a longer retention period.

Answer

Create new consumer applications that read from the stream.

Question 2

A company uses Amazon Kinesis Data Streams with a shard count of 10 to ingest clickstream data. The data is consumed by a Lambda function that transforms the records and writes to Amazon S3. Recently, the Lambda function started failing with 'ProvisionedThroughputExceededException' errors. The average record size is 5 KB, and the incoming data rate is 15 MB/s. What is the most likely cause and solution?

Accepted Answer

Increase the number of shards in the Kinesis data stream to 15.. The ProvisionedThroughputExceededException indicates that the total write throughput to the Kinesis stream is exceeding the provisioned capacity. With 10 shards, the maximum write throughput is 10 MB/s (1 MB/s per shard). The incoming data rate is 15 MB/s, which exceeds this limit, causing the error. Increasing the shard count to 15 raises the write throughput to 15 MB/s, matching the incoming rate and resolving the issue.

Answer

Decrease the batch size of the Lambda event source mapping.

Answer

Increase the Lambda function's memory allocation to 3008 MB.

Answer

Increase the Lambda function's reserved concurrency.

Question 3

A data engineer needs to ingest streaming data from thousands of IoT devices into AWS for real-time processing. The data volume peaks at 5 GB/min. Which AWS service should be used as the ingestion endpoint?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time data ingestion at scale, supporting throughput of up to 1 MB/s or 1,000 records/s per shard. With a peak of 5 GB/min (~83 MB/s), you can horizontally scale by adding shards to meet the required throughput, making it the ideal ingestion endpoint for high-volume streaming IoT data.

Answer

AWS Glue

Answer

Amazon S3

Answer

AWS Lambda

Question 4

A data engineer needs to set up a data ingestion pipeline that reads from Amazon MSK (Managed Streaming for Kafka) and writes to Amazon S3 with transformations. The data is in Avro format and must be converted to Parquet. Which THREE components should be used together? (Choose THREE.)

Accepted Answer

AWS Lambda function to convert Avro to Parquet as a Firehose transformation. AWS Lambda can be used as a transformation function within a Kinesis Data Firehose delivery stream to convert Avro records to Parquet format before delivery to S3. This is a serverless, real-time approach that integrates directly with Firehose's transformation capabilities, avoiding the need for separate compute resources.

Answer

Amazon Athena to convert the data format

Answer

AWS Glue ETL job to read from MSK

Question 5

A company is building a data lake on Amazon S3 and needs to ingest data from multiple sources. Which of the following AWS services can be used to ingest and transform data in near real-time? (Select TWO.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL (Extract, Transform, Load) service that can ingest data from various sources and transform it in near real-time using its streaming ETL capabilities. Glue can consume data from Amazon Kinesis Data Streams or Apache Kafka, apply transformations using Apache Spark, and write the results to Amazon S3 or other destinations, making it suitable for near real-time data ingestion and transformation.

Answer

Amazon Athena

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AWS Step Functions

Answer

Amazon Simple Queue Service (SQS)

Question 6

A company stores financial data in Amazon RDS for MySQL. They need to retain backups for 7 years to meet compliance. Which backup strategy meets this requirement?

Accepted Answer

Use the AWS Backup service with a 7-year retention policy. AWS Backup is the correct service for long-term retention of RDS snapshots beyond the 35-day limit of automated backups. It allows you to create backup plans with retention policies up to 100 years, making it suitable for the 7-year compliance requirement. Manual snapshots can also be retained indefinitely, but AWS Backup provides centralized management and lifecycle policies.

Answer

Use read replicas to retain data

Answer

Take daily manual snapshots and delete after 7 years

Answer

Enable automated backups with a retention period of 7 years

Question 7

A data engineer needs to store log files from multiple applications in a central S3 bucket. The logs must be stored cost-effectively for long-term retention (7 years). The logs are accessed infrequently after the first 30 days. Which storage class should the engineer use for objects older than 30 days?

Accepted Answer

S3 Standard-IA. D is correct because S3 Standard-IA (Infrequent Access) is designed for data accessed less frequently but requires rapid access when needed, with a lower storage cost than S3 Standard and a 30-day minimum storage duration charge. After the first 30 days, logs are infrequently accessed, making Standard-IA the most cost-effective option that still provides millisecond first-byte latency for occasional retrieval needs over the 7-year retention period.

Answer

S3 Glacier Deep Archive

Answer

S3 Standard

Answer

S3 One Zone-IA

Question 8

A company is migrating on-premises Apache Kafka clusters to Amazon MSK. The migration must be seamless with no data loss. The team is using MirrorMaker 2 to replicate data from on-premises Kafka to MSK. Which THREE configurations are necessary to ensure exactly-once semantics and minimal downtime? (Choose three.)

Accepted Answer

Set offsets.topic.replication.factor to 3 for the consumer offsets topic.. To ensure exactly-once semantics and minimal downtime when migrating on-premises Kafka to Amazon MSK using MirrorMaker 2, three configurations are essential. First, setting `offsets.topic.replication.factor` to 3 (option B) ensures that consumer offsets are replicated across multiple brokers, preventing loss of offset information during broker failures, which supports exactly-once semantics by maintaining accurate consumer state. Second, setting `replication.factor` to 3 on the MSK cluster (option C) increases data durability and availability, reducing the risk of data loss during the migration. Third, configuring MirrorMaker 2 to use exactly-once semantics (option E) prevents duplicate message delivery by enabling idempotent producers and transactional semantics in the replication flow. Options A and D are not required: `auto.create.topics.enable` should typically be left as default (true) to allow dynamic topic creation, and TLS encryption (option D) is a security measure but not necessary for exactly-once semantics or minimal downtime.

Answer

Set auto.create.topics.enable to false to prevent automatic topic creation.

Answer

Enable TLS encryption between on-premises and MSK.

Question 9

A data engineer is setting up Amazon S3 bucket policies for a data lake. Which TWO statements are true regarding S3 bucket policies? (Choose TWO.)

Accepted Answer

Bucket policies can grant access to accounts in other AWS Organizations. S3 bucket policies can grant cross-account access to principals in other AWS accounts, including those in different AWS Organizations, by specifying the target account ID or organization ID in the Principal element. This enables centralized data lake access management across organizational boundaries without requiring IAM roles or resource-based policies in each account.

Answer

Bucket policies are the only way to control access to S3

Answer

Bucket policies can be applied to individual objects

Answer

The Principal element in a bucket policy is optional

Question 10

A company runs an Amazon RDS for MySQL database. The database experiences high write latency during peak hours. The data engineer notices that the WriteIOPS metric is consistently at the provisioned limit. Which action would most effectively reduce write latency without increasing costs?

Accepted Answer

Migrate to Amazon Aurora MySQL with appropriate instance size. Migrating to Amazon Aurora MySQL with an appropriate instance size reduces write latency because Aurora’s distributed storage architecture provides up to 20 times the write throughput of standard MySQL on RDS, without requiring additional IOPS provisioning. Aurora automatically scales storage I/O and uses a 6-replica quorum-based write model, which eliminates the bottleneck of hitting a fixed IOPS limit while keeping costs comparable to or lower than provisioned IOPS on RDS.

Answer

Enable Multi-AZ deployment

Answer

Increase the provisioned IOPS on the existing RDS instance

Answer

Add a read replica to offload read traffic

Question 11

A data engineering team is migrating a MySQL database to Amazon RDS for MySQL. They need to ensure high availability and automated failover. Which THREE configurations should they implement?

Accepted Answer

Enable automated backups with a retention period.. Automated backups with a retention period enable point-in-time recovery (PITR) and are required for Multi-AZ failover to function properly. RDS uses automated backups to keep the standby instance synchronized and to support recovery after a failover event.

Answer

Enable Enhanced Monitoring.

Answer

Create a read replica in a different region.

Question 12

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is then processed by a Kinesis Data Analytics application running SQL queries. The analytics application is falling behind and processing records with increasing latency. The stream has 4 shards, and the average record size is 5 KB. What is the MOST effective way to improve processing latency?

Accepted Answer

Increase the parallelism of the Kinesis Data Analytics application.. Increasing the parallelism of the Kinesis Data Analytics application (e.g., by increasing the number of in-application streams or ParallelismPerKPU) allows it to consume from the stream faster, reducing latency. Option A is wrong because 5 KB is well below the 1 MB/s shard limit, so increasing shards is unnecessary. Option B is wrong because enhanced fan-out is for consumers that need low latency, but does not help if the application is CPU-bound. Option D is wrong because increasing the retention period does not affect processing speed; it only keeps data longer.

Answer

Increase the number of shards in the Kinesis stream to 8.

Answer

Enable enhanced fan-out on the Kinesis stream for the analytics application.

Answer

Increase the retention period of the Kinesis stream to 7 days.

Question 13

A data engineer is monitoring an Amazon Kinesis Data Stream with a shard count of 10. The stream receives 5 MB/s of write traffic and 10 MB/s of read traffic. The engineer notices that writes are throttled with ProvisionedThroughputExceededException errors. Which action should the engineer take to resolve the throttling?

Accepted Answer

Configure auto-scaling on the stream.. ProvisionedThroughputExceededException occurs when a shard's write throughput exceeds 1 MB/s, often due to hot shards from uneven partition key distribution. While increasing shard count (Option A) can help spread the load, it does not automatically fix the root cause if partition keys remain skewed. The best action is to configure auto-scaling (Option D), which in Amazon Kinesis Data Streams can be achieved by switching to on-demand mode. On-demand mode automatically scales capacity based on traffic patterns, eliminating throttling without manual intervention. Option B decreases write capacity, worsening the issue. Option C only improves read throughput, not write.

Answer

Increase the shard count to 20.

Answer

Decrease the shard count to 5.

Answer

Enable enhanced fan-out on the stream.

Question 14

A data engineer is designing a data pipeline that ingests streaming data from an IoT fleet using Kinesis Data Streams and processes it with a Lambda function. The Lambda function often times out when the data volume spikes. What is the most scalable solution?

Accepted Answer

Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.. Increasing the number of shards increases the parallelism of the stream, allowing higher throughput. Using Kinesis Data Analytics for windowed aggregation reduces the volume of data sent to Lambda, preventing timeouts during spikes. Option A is wrong because reducing batch size decreases the number of records per invocation, which can increase the number of Lambda invocations and overhead, but does not address the root cause of timeouts due to volume. Option B is wrong because increasing the Lambda timeout to 15 minutes allows the function to run longer, but it does not increase throughput or handle spikes efficiently; it only delays failures and is not scalable. Option C is wrong because increasing memory can improve performance, but setting reserved concurrency limits the maximum number of concurrent executions, which can throttle processing during spikes and reduce scalability.

Answer

Reduce the batch size in the event source mapping.

Answer

Increase the Lambda function timeout to 15 minutes.

Answer

Increase the Lambda function memory and set reserved concurrency.

Question 15

Refer to the exhibit. A data engineer creates an Amazon Redshift table with the above DDL. The engineer runs a query to find all orders for a specific customer within a date range. Which statement about query performance is correct?

Accepted Answer

The query will benefit from both the distribution key and the sort key to minimize data scanned.. The DDL defines customer_id as the distribution key and order_date as the sort key. When the query filters on both customer_id (distribution key) and order_date (sort key), Redshift can use partition pruning via the sort key to skip blocks that don't match the date range, and the distribution key ensures that data for the same customer is co-located on the same node slice, minimizing data movement. This combination reduces the amount of data scanned and improves query performance.

Answer

The query will be inefficient because the distribution key is not the same as the sort key.

Answer

The table should use DISTSTYLE EVEN to improve performance.

Answer

The sort key will not help because the query filters on customer_id first.

Question 16

A company uses Amazon S3 as its data lake. A data engineer needs to enforce encryption of data at rest using server-side encryption with AWS KMS. Which S3 bucket property should be configured?

Accepted Answer

Default encryption. Configuring default encryption on an S3 bucket ensures that all objects stored in the bucket are encrypted at rest using server-side encryption. When AWS KMS is specified as the encryption type, S3 automatically encrypts objects with a KMS key (SSE-KMS) upon upload, even if the upload request does not include encryption headers. This enforces encryption at rest without requiring changes to client applications.

Answer

Server access logging

Answer

Versioning

Answer

Bucket policy

Question 17

A data engineer is optimizing an Amazon RDS for MySQL database that experiences high write throughput. The engineer wants to improve write performance and reduce latency. Which TWO database-level configuration changes can help achieve this?

Accepted Answer

Use Provisioned IOPS (io1 or io2) storage.. Provisioned IOPS (io1 or io2) storage delivers consistent and predictable I/O performance by guaranteeing a specified number of I/O operations per second, which directly reduces latency and improves write throughput for high-write workloads. This is the most effective storage-level change for write-intensive RDS for MySQL databases.

Answer

Reduce the backup retention period to 1 day.

Answer

Create a Read Replica to offload writes.

Answer

Enable Multi-AZ for high availability.

Question 18

A company is ingesting real-time clickstream data into Amazon S3 using Amazon Kinesis Data Firehose. The data is semi-structured and the company wants to transform the data into Parquet format and partition it by year, month, day, and hour. Which TWO steps should be taken to achieve this? (Choose TWO.)

Accepted Answer

Enable dynamic partitioning in Kinesis Data Firehose and specify the partition keys as year, month, day, hour extracted from the data.. Kinesis Data Firehose's dynamic partitioning feature allows you to specify partition keys (year, month, day, hour) extracted from the incoming data, and Firehose will automatically create the corresponding S3 prefix structure (e.g., year=2024/month=01/day=15/hour=10/) during delivery. Option D is correct because to convert semi-structured data to Parquet format, you can attach an AWS Lambda function as a data transformation to Firehose, which converts each record to Parquet before delivery to S3. Option E is incorrect because while Kinesis Data Firehose does support converting data to Parquet format using a schema from the AWS Glue Data Catalog, this approach requires a pre-defined Glue schema and is less flexible for semi-structured data. Moreover, the question does not mention any existing Glue Data Catalog, and the Lambda transformation in option D is a more direct and customizable method for the transformation needed.

Answer

Set up an Amazon S3 event notification to trigger an AWS Lambda function that partitions the data after delivery.

Answer

Use an AWS Glue Crawler to infer the schema and automatically partition the data in S3.

Answer

Configure Kinesis Data Firehose to convert the data to Parquet format using a schema from the AWS Glue Data Catalog.

Question 19

A company uses AWS DMS to migrate a 2 TB Oracle database to Amazon RDS for PostgreSQL. The migration is taking longer than expected. The task status shows 'Full load in progress' with a low 'Table throughput (rows/s)'. Which action would MOST improve throughput?

Accepted Answer

Increase the number of parallel tasks in the DMS task settings. The low 'Table throughput (rows/s)' during the full load phase indicates that the DMS task is not processing tables with enough parallelism. Increasing the number of parallel tasks in the DMS task settings allows the replication instance to load multiple tables concurrently, which directly improves throughput by utilizing available CPU and memory resources more efficiently.

Answer

Enable Multi-AZ on the DMS replication instance

Answer

Change the target table preparation mode to 'Do nothing'

Answer

Increase the number of shards in the source database

Question 20

A company uses AWS Glue ETL to transform data from Amazon RDS for MySQL to Amazon S3. The Glue job reads from a JDBC connection. The job runs once daily and processes all records, but the data volume is growing. Which change would improve performance and reduce costs?

Accepted Answer

Enable Glue job bookmarking and set the job to process only new data. Enabling Glue job bookmarking allows the job to process only new or changed data since the last run, rather than reprocessing the entire dataset. This reduces both the data volume read from the JDBC source and the transformation time, directly improving performance and lowering costs by minimizing DPU usage.

Answer

Increase the number of DPUs for the Glue job

Answer

Switch to a Glue Python shell job

Answer

Use a higher JDBC fetch size