DEA-C01 Practice Exam 58 — 20 Questions

Question 1

A data engineer is designing a streaming pipeline that ingests IoT sensor data from 10,000 devices. Each device sends a 1 KB message every second. The data must be processed in near real-time and stored in S3 for analytics. Which combination of services provides the most cost-effective solution?

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

Accepted Answer

Amazon Kinesis Data Streams with Kinesis Data Firehose delivery to S3.. B is correct because Kinesis Data Streams ingests high-throughput IoT data (10,000 messages/sec at 1 KB each) with low latency, and Kinesis Data Firehose automatically batches and compresses data before delivering it to S3, eliminating the need for custom code or manual scaling. This combination provides the most cost-effective near-real-time solution by leveraging Firehose's built-in buffering and compression to minimize S3 storage costs and reduce the number of PUT requests.

Answer

AWS Data Pipeline with periodic S3 copy.

Answer

Amazon MSK (Managed Streaming for Kafka) with Kafka Connect S3 sink.

Answer

Amazon SQS FIFO queue with Lambda consumers writing to S3.

Question 2

A data engineer needs to securely store database credentials for an RDS instance. Which TWO AWS services can be used?

Accepted Answer

AWS Secrets Manager. AWS Secrets Manager (B) is correct because it is purpose-built to store, rotate, and retrieve secrets such as RDS database credentials, and it natively integrates with RDS for automatic credential rotation. AWS Systems Manager Parameter Store (E) is also correct because it can store database credentials as SecureString parameters, which are encrypted with AWS KMS, allowing the data engineer to retrieve them securely at runtime. AWS KMS (A) only provides encryption keys and cryptographic operations; it does not itself store credentials or secrets. AWS IAM (C) manages identities, roles, and permissions, not secret values. AWS CloudFormation (D) is an infrastructure-as-code provisioning service and is not designed to store or retrieve database credentials.

Answer

AWS KMS

Answer

AWS IAM

Answer

AWS CloudFormation

Question 3

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 in Amazon Redshift rely on zone maps that must be refreshed via VACUUM REINDEX after significant data changes. Without reindexing, the zone maps become stale and the query planner cannot skip blocks, forcing a sequential scan instead of a range-restricted scan. Running VACUUM REINDEX rebuilds the interleaved sort metadata so range predicates can prune blocks effectively.

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 4

A data engineer is troubleshooting an Amazon Redshift cluster that has been experiencing slow query performance. The engineer checks the system tables and finds that many queries are waiting on 'wlm_queued' time. The cluster has 10 nodes and uses automatic WLM. What is the most likely cause?

Accepted Answer

The number of concurrent queries exceeds the available query slots.. When queries show 'wlm_queued' time in Amazon Redshift, it indicates they are waiting in the Workload Management (WLM) queue before execution. With automatic WLM, the system dynamically manages concurrency, but if the number of concurrent queries exceeds the available query slots (determined by the cluster's memory and concurrency scaling settings), queries will be queued. This is the most direct cause of 'wlm_queued' wait events, as WLM queues queries when all slots are occupied.

Answer

Network bandwidth saturation between nodes.

Answer

Sorting operations are too expensive.

Answer

Insufficient disk space on the cluster.

Question 5

A company is migrating an on-premises MySQL database to Amazon RDS for MySQL. The database is 500 GB in size. The migration must have minimal downtime and must be completed within a week. Which AWS service should the data engineer use to perform the migration?

Accepted Answer

AWS Database Migration Service (DMS).. AWS Database Migration Service (DMS) is the correct choice because it is specifically designed for migrating databases to AWS with minimal downtime. DMS can perform a full load of the 500 GB MySQL database and then continuously replicate ongoing changes from the on-premises source to the Amazon RDS target using change data capture (CDC), allowing the migration to complete within a week with near-zero downtime.

Answer

Amazon S3 Transfer Acceleration.

Answer

AWS Snowball Edge.

Answer

AWS DataSync.

Question 6

A data engineering team needs to transform CSV files stored in Amazon S3 into Parquet format using AWS Glue. The files are partitioned by date and are updated hourly. Which AWS Glue feature should be used to automatically detect the schema and partition structure?

Accepted Answer

AWS Glue Crawler. AWS Glue Crawler is the correct choice because it automatically scans data in S3, infers the schema (including data types), and detects the partition structure (e.g., date-based partitions like year/month/day) by examining the folder hierarchy. It then populates the AWS Glue Data Catalog with metadata, enabling ETL jobs to read the data without manual schema definition.

Answer

AWS Glue DataBrew

Answer

AWS Lake Formation

Answer

Amazon Athena

Question 7

A data engineer is designing a data ingestion pipeline for IoT sensor data. The sensors send JSON messages every second. The data must be available in Amazon S3 within 5 minutes and must be transformed (JSON to Parquet) before storage. Which combination of services meets these requirements?

Accepted Answer

Amazon Kinesis Data Firehose with data transformation and Parquet conversion. Amazon Kinesis Data Firehose natively supports AWS Lambda-based record transformation and can convert the transformed JSON records to Parquet using its built-in format conversion before delivering to S3. Firehose buffers data and delivers within its configurable buffer interval (as low as 60 seconds), comfortably meeting the 5-minute SLA. This is the only option that combines ingestion, transformation, and Parquet conversion in a single managed service.

Answer

Amazon Kinesis Data Streams with AWS Glue streaming ETL

Answer

Amazon Kinesis Data Analytics with output to S3

Answer

Amazon S3 with S3 Event Notifications to AWS Lambda for transformation

Question 8

A company needs to enforce encryption in transit for all data moving between its Amazon S3 bucket and a fleet of Amazon EC2 instances. The data is accessed via S3 API calls over the internet. Which configuration ensures encryption in transit?

Accepted Answer

Configure the bucket policy to deny requests that do not use HTTPS.. Configuring the bucket policy to deny requests that do not use HTTPS ensures encryption in transit for S3 API calls. Option A is wrong because SSE-S3 encrypts data at rest, not in transit. Option B is wrong because S3 Transfer Acceleration uses a global network but does not enforce encryption; HTTPS must still be used. Option C is wrong because a VPC endpoint for S3 uses AWS network but does not enforce encryption; the bucket policy must explicitly require HTTPS.

Answer

Enable SSE-S3 on the bucket.

Answer

Enable S3 Transfer Acceleration.

Answer

Use a VPC endpoint for S3.

Question 9

A company wants to ingest streaming data from thousands of IoT devices into AWS for real-time processing. Each device sends JSON payloads of about 2 KB at a rate of 1 message per second. The data must be processed with a durable, ordered stream per device. Which service should the company use as the ingestion layer?

Accepted Answer

Amazon Kinesis Data Streams.. Amazon Kinesis Data Streams is the correct choice because it provides durable, ordered stream processing per shard, which can be partitioned by device ID to maintain message order for each device. It supports real-time ingestion from thousands of IoT devices at 2 KB per message per second, with a retention period of up to 365 days and the ability to reprocess data via multiple consumers.

Answer

Amazon Simple Queue Service (Amazon SQS) with a FIFO queue.

Answer

Amazon Simple Notification Service (Amazon SNS) with a Lambda subscriber.

Answer

Amazon Kinesis Data Firehose with Direct Put.

Question 10

A retail company uses Amazon Kinesis Data Firehose to ingest clickstream data from its website into an Amazon S3 bucket. The data includes fields: user_id, event_type, timestamp, page_url. Recently, the data engineering team noticed that some records have malformed JSON (missing commas, extra brackets) causing delivery failures to S3. The Firehose delivery stream is configured to retry failed records for 300 seconds, after which the records are sent to an S3 bucket for failed records. The team wants to transform the data to correct malformed JSON before delivery to the main S3 bucket. They need a solution that does not require managing servers and can handle high throughput. What should the team do?

Accepted Answer

Configure an AWS Lambda function as a data transformation in Kinesis Data Firehose to correct malformed JSON.. Kinesis Data Firehose supports Lambda-based data transformation natively: you attach a Lambda function to the delivery stream, and Firehose invokes it on each batch before delivery. This is serverless, scales with throughput, and lets you parse and repair malformed JSON before it reaches the main S3 bucket.

Answer

Set up an Amazon EMR cluster with Apache Spark to process the data in micro-batches and fix JSON errors.

Answer

Use an AWS Glue streaming ETL job to read from Firehose and write corrected data to S3.

Answer

Use Amazon Kinesis Data Analytics with a SQL application to parse and fix JSON.

Question 11

A company stores IoT sensor data in S3 as JSON files. They need to convert the data to Parquet format for efficient querying with Amazon Athena. Which AWS service can perform this transformation with minimal effort?

Accepted Answer

AWS Glue ETL job. AWS Glue ETL jobs can easily convert JSON to Parquet with built-in transforms. Option A is wrong because Kinesis Data Firehose is for streaming data ingestion, not batch transformations. Option B is wrong because Amazon Athena is a query engine, not a transformation service. Option D is wrong because AWS Lambda is for small, event-driven transformations and is not ideal for large-scale batch conversion.

Answer

Kinesis Data Firehose

Answer

Amazon Athena

Answer

AWS Lambda

Question 12

A data engineer is designing a streaming pipeline that ingests IoT sensor data from 10,000 devices. Each device sends a 1 KB message every second. The data must be processed in near real-time and stored in S3 for analytics. Which combination of services provides the most cost-effective solution?

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

Accepted Answer

Switch the Kinesis stream to on-demand capacity mode. Kinesis Data Streams on-demand capacity mode automatically scales shard capacity up or down based on observed throughput, eliminating the need to manually provision or add shards. It handles the 50% growth without any CloudWatch alarms, Lambda functions, or Auto Scaling groups. This is the only option that provides fully managed, hands-off scaling.

Answer

Set up a CloudWatch Alarm to trigger a Lambda function to add shards

Answer

Use an Auto Scaling group to add more shards

Answer

Configure the stream to use a Lambda function that scales shards

Question 13

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 automatically updates to point to the new primary instance in the standby Availability Zone. If the application hardcodes the old primary instance's endpoint (specific IP or DNS name) instead of using the RDS CNAME (which remains constant), it will attempt to connect to the failed instance, causing connectivity loss. The CNAME is the stable connection point that always resolves to the current primary instance.

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 14

A company is migrating an on-premises Apache Cassandra database to Amazon Keyspaces. The database has a table with a partition key of 'user_id' and a clustering column of 'timestamp'. The application frequently queries the last 10 records for a given user. Which table design in Keyspaces would provide the BEST query performance for this access pattern?

Accepted Answer

Partition key: user_id, clustering column: timestamp (descending order).. It preserves the original Cassandra table design with 'user_id' as the partition key and 'timestamp' as the clustering column in descending order. This allows Keyspaces to efficiently retrieve the last 10 records for a given user by performing a range query on the clustering column within a single partition, avoiding full table scans or cross-partition queries.

Answer

Partition key: random column, clustering column: none.

Answer

Partition key: timestamp, clustering column: user_id.

Answer

Partition key: user_id, clustering column: none.

Question 15

A company wants to ingest streaming data from thousands of IoT devices into Amazon S3 with minimal latency and then transform the data using Spark SQL. Which AWS service should be used for data ingestion?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed for ingesting streaming data into Amazon S3 with near-real-time latency (typically 60 seconds or less). It can directly write data to S3 without requiring custom code or additional infrastructure, and it supports optional transformations via AWS Lambda, making it ideal for the described use case of streaming IoT data ingestion.

Answer

Amazon EMR

Answer

AWS Glue

Answer

Amazon Athena

Question 16

A company runs a real-time analytics platform on Amazon ECS that ingests streaming data from Amazon Kinesis Data Streams, processes it, and stores results in Amazon DynamoDB. The data volume spikes unpredictably, causing DynamoDB to throttle write requests. The application uses on-demand capacity mode. The data engineer notices that the throttling occurs on a specific partition due to a hot key. The hot key is a customer ID that receives a disproportionate number of writes. The application cannot change the partition key design immediately. The engineer needs to reduce throttling while maintaining low latency. Which solution is most effective?

Accepted Answer

Implement a write buffer using Amazon SQS, and have consumers write to DynamoDB at a controlled rate.. Buffering writes through Amazon SQS decouples the ingestion rate from DynamoDB's capacity, allowing consumers to write at a controlled pace. This directly mitigates throttling on the hot key without requiring a partition key redesign, and SQS provides low-latency, durable buffering suitable for real-time analytics.

Answer

Switch to provisioned capacity with auto scaling and increase the write capacity units.

Answer

Enable DynamoDB Accelerator (DAX) to cache the hot key writes.

Answer

Use DynamoDB Streams to trigger a Lambda function that retries throttled writes.

Question 17

A company uses Amazon Redshift for data warehousing. The security team requires that all data in transit between the Redshift cluster and clients be encrypted. Which feature should be enabled?

Accepted Answer

SSL/TLS encryption. Amazon Redshift supports SSL/TLS encryption for client connections to ensure data in transit is encrypted. Option A (Client-side VPN) is not a Redshift feature for encrypting client connections. Option C (AWS KMS key) is used for encrypting data at rest, not in transit. Option D (VPC peering) does not provide encryption of data in transit between the cluster and clients.

Answer

Client-side VPN

Answer

AWS KMS key

Answer

VPC peering

Question 18

A data engineer is designing a data ingestion pipeline for real-time financial transactions. The pipeline must ensure exactly-once processing semantics and must handle duplicate records that may occur due to retries. Which combination of AWS services can achieve exactly-once processing?

Accepted Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Streams with Kinesis Data Analytics for Apache Flink enables exactly-once processing by leveraging Flink's built-in checkpointing and two-phase commit protocols. Kinesis Data Streams provides a durable, ordered record store, while Flink uses its internal state and idempotent sinks to deduplicate records that arise from retries, ensuring each record is processed exactly once.

Answer

Amazon SQS with AWS Lambda

Answer

Amazon MSK with AWS Lambda

Answer

Amazon Kinesis Data Firehose with AWS Lambda

Question 19

A company wants to ingest real-time clickstream data from a website into Amazon S3 with a maximum latency of 60 seconds. The data volume peaks at 500 MB/s. Which service should they use to buffer and deliver the data to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is designed to ingest streaming data, buffer it, and deliver it to destinations like Amazon S3 with configurable buffer intervals (e.g., 60 seconds) and buffer sizes (e.g., up to 128 MB). It can handle the peak throughput of 500 MB/s by automatically scaling, and it meets the maximum latency requirement of 60 seconds by flushing data to S3 based on time or size thresholds.

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams

Answer

AWS Lambda

Question 20

A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3 in Parquet format. The replication is used for near-real-time analytics. Recently, the DMS task started failing with an error indicating insufficient memory. The source database is large (2 TB). What should a data engineer do to resolve this issue while minimizing changes to the existing architecture?

Accepted Answer

Increase the DMS replication instance size.. The error indicates the DMS replication instance is running out of memory during continuous replication of a 2 TB Oracle database to S3 in Parquet format. Increasing the replication instance size (Option D) directly addresses the memory constraint by providing more RAM and processing capacity, which is necessary for handling large volumes of Change Data Capture (CDC) data and Parquet conversion overhead. This solution requires minimal architectural changes, as it only involves modifying the instance class in the DMS task settings.

Answer

Change the target format to JSON to reduce memory usage.

Answer

Split the DMS task into multiple smaller tasks.

Answer

Use Change Data Capture (CDC) only, without full load.