DEA-C01 Practice Exam 55 — 20 Questions

Question 1

A company uses AWS Glue to process data from multiple S3 buckets. The Glue job runs daily and reads data from a bucket that contains millions of small files (each < 1 MB). The job has been running for hours and is often close to the 8-hour timeout limit. Which optimization would MOST reduce the job's runtime?

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

Accepted Answer

Pre-process the data to consolidate small files into larger files before the Glue job.. Consolidating millions of small files (<1 MB each) into larger files before the Glue job is the most impactful optimization because Spark and Glue incur massive per-file overhead: each file requires a separate S3 GET, task scheduling, and metadata operation. With millions of tiny files, the job spends most of its time on I/O and task overhead rather than actual processing. Merging them into a few hundred MB files dramatically reduces task count and runtime.

Answer

Convert the source data from CSV to Parquet format.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Use a larger Spark shuffle partition size.

Question 2

A company uses Amazon S3 to store raw data and runs AWS Glue ETL jobs to transform it into Parquet. The data is then queried using Amazon Athena. Queries are slow and expensive due to high scan volumes. Which THREE design changes can improve query performance and reduce costs? (Select THREE.)

Accepted Answer

Convert the data to a columnar format like Parquet or ORC if not already. Option B is correct because columnar formats such as Parquet or ORC let Athena read only the columns referenced in a query, dramatically reducing the bytes scanned compared to row-based formats like CSV or JSON. Option C is correct because splittable compression such as Snappy (or Zstandard) reduces storage and scan volume while still allowing Athena to split large files across parallel readers, unlike non-splittable gzip on a single object. Option E is correct because partitioning by frequently filtered columns such as date or region enables partition pruning, so Athena skips reading irrelevant S3 prefixes and scans far less data. Option A is not appropriate because shrinking files to 1 MB creates a huge number of small files, increasing S3 LIST and request overhead and hurting, not helping, Athena performance. Option D is not appropriate here because bucketing on high-cardinality columns does not reduce the data scanned by Athena and is generally used for join optimization in Hive/Spark, not for lowering Athena scan costs.

Answer

Increase the number of files by reducing file size to 1 MB

Answer

Use bucketing on high-cardinality columns

Question 3

A company is using AWS KMS to encrypt data in Amazon S3. The security team wants to ensure that only specific IAM roles can decrypt the data. Which TWO steps should the data engineer take? (Choose two.)

Accepted Answer

Create a customer-managed KMS key with a key policy that grants kms:Decrypt only to the allowed IAM roles. Option C is correct because a customer-managed KMS key allows you to define a key policy that explicitly grants kms:Decrypt only to the specific IAM roles, which is the core mechanism for restricting decryption permissions in KMS. Option E is correct because the S3 bucket must be configured to use SSE-KMS with that customer-managed key; otherwise, S3 would use a different key (such as the default aws/s3 key) and the key policy restriction would not apply to the objects. Option A is incorrect because the default AWS managed key aws/s3 has a key policy managed by AWS that grants broad permissions to the account, so it cannot be scoped to only specific IAM roles. Option B is incorrect because SSE-S3 uses AES-256 with S3-managed keys and does not involve KMS or IAM role-based decrypt permissions at all. Option D is incorrect because requiring MFA for kms:Decrypt adds an authentication condition but does not by itself limit decryption to specific IAM roles, and MFA is not the mechanism that enforces role-level restriction.

Answer

Use the default AWS managed KMS key for S3 (aws/s3)

Answer

Use SSE-S3 encryption instead of KMS

Answer

Add an IAM policy to the role that requires MFA for kms:Decrypt

Question 4

A company is building a data pipeline that ingests sensitive customer data from an on-premises database into Amazon S3 using AWS DMS. The data must be encrypted at rest in S3 and in transit. The security team requires that the encryption keys be managed by the company (not AWS). Which TWO actions should the data engineer take to meet these requirements? (Choose TWO.)

Accepted Answer

Configure the S3 bucket to use server-side encryption with AWS KMS (SSE-KMS) using a customer managed key.. Option B is correct because SSE-KMS with a customer managed key lets the company own and control the KMS key that protects the data at rest in Amazon S3, satisfying the requirement that keys be managed by the company rather than AWS. Option D is correct because enabling SSL/TLS on the DMS source and target endpoints encrypts data in transit between the on-premises database and Amazon S3, which is the required in-transit protection. Option A is incorrect because the default DMS encryption settings do not provide company-managed keys for S3 data at rest. Option C is incorrect because SSE-S3 uses S3-managed keys, which AWS controls, not the company. Option E is incorrect because KMS keys are used for encryption at rest, not for encrypting DMS data in transit; in-transit encryption is handled by SSL/TLS on the endpoints.

Answer

Enable encryption at rest using the default DMS encryption settings.

Answer

Configure the S3 bucket to use server-side encryption with S3 managed keys (SSE-S3).

Answer

Create an AWS KMS key and use it in the DMS endpoint to encrypt data in transit.

Question 5

A company uses Amazon RDS for MySQL with Multi-AZ deployment. The primary instance fails, and automatic failover occurs. After failover, the application experiences higher latency. What is the most likely cause?

Accepted Answer

The new primary instance is in a different Availability Zone, increasing network latency.. After a Multi-AZ failover in Amazon RDS for MySQL, the new primary instance is launched in a different Availability Zone (AZ) than the original primary. If the application's compute resources (e.g., EC2 instances) remain in the original AZ, cross-AZ network traffic incurs additional latency due to the physical distance and the need to traverse the AZ boundary, which typically adds 1–2 ms of round-trip time. This increased network latency directly impacts application performance, especially for latency-sensitive queries.

Answer

The read replica is now the primary and cannot handle write traffic.

Answer

The failover process disabled automatic backups.

Answer

The DNS endpoint did not update to point to the new primary.

Question 6

A company has an Amazon RDS for MySQL DB instance with read replicas. The primary DB instance fails. What is the correct procedure to promote a read replica to become the new primary?

Accepted Answer

Manually promote the read replica to a standalone DB instance.. When an Amazon RDS for MySQL primary DB instance fails, read replicas do not automatically become the new primary. The correct procedure is to manually promote the read replica using the AWS Management Console, CLI, or API, which converts it into a standalone DB instance. After promotion, you must update your application endpoints to point to the new primary, as RDS does not handle this automatically.

Answer

Modify the read replica to be a Multi-AZ deployment and failover will occur.

Answer

RDS automatically fails over to the read replica within 5 minutes.

Answer

Delete the primary and the read replica will automatically become the primary.

Question 7

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.)

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 8

A company needs to monitor and record all changes to IAM policies in their AWS account. Which AWS service should be used?

Accepted Answer

AWS CloudTrail. AWS CloudTrail records API activity in an AWS account, including all IAM policy changes (CreatePolicy, AttachRolePolicy, PutRolePolicy, etc.), capturing who made the change, when, from which IP, and with what parameters. It is the authoritative audit service for governance, compliance, and security analysis of control-plane actions. CloudWatch Logs, GuardDuty, and IAM Access Analyzer serve monitoring, threat detection, and permission analysis—not comprehensive API change recording.

Answer

Amazon CloudWatch Logs

Answer

Amazon GuardDuty

Answer

AWS IAM Access Analyzer

Question 9

A company runs an Amazon EMR cluster with Spark jobs that process data from Amazon S3. The data engineer receives an alert that one of the Spark jobs failed with an OutOfMemoryError. The job processes large files and uses the default Spark configurations. Which configuration change is MOST likely to resolve the issue?

Accepted Answer

Increase the spark.executor.memory configuration.. An OutOfMemoryError in Spark typically occurs when an executor's JVM heap is exhausted during processing of large partitions or files. Increasing spark.executor.memory directly expands the heap available to each executor, allowing it to hold more data in memory and resolve the OOM condition.

Answer

Increase the number of executors.

Answer

Disable dynamic resource allocation.

Answer

Decrease the number of cores per executor.

Question 10

A data engineer needs to store log files from multiple applications in a centralized location. The logs are generated in JSON format and each log entry is about 1 KB. The engineer needs to query the logs occasionally using SQL-like queries. Which AWS service is most appropriate?

Accepted Answer

Amazon Athena with data stored in S3. Amazon Athena is the most appropriate service because it allows you to query log files stored in S3 directly using standard SQL, without needing to load or transform the data. Since the logs are in JSON format and each entry is about 1 KB, Athena's schema-on-read approach works perfectly for occasional SQL-like queries, and you only pay for the data scanned per query, making it cost-effective for infrequent access.

Answer

Amazon DynamoDB

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Question 11

A data engineer needs to store JSON documents that are frequently read and written by a web application. The data has a flexible schema and requires low-latency queries on primary key lookups. Which AWS service is MOST suitable?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the most suitable service because it is a NoSQL key-value and document database that provides single-digit millisecond latency for primary key lookups, supports flexible schemas for JSON documents, and is designed for high-throughput read/write workloads from web applications. Its fully managed nature and auto-scaling capabilities align with the requirement for frequent, low-latency queries on a flexible schema.

Answer

Amazon Redshift

Answer

Amazon S3

Answer

Amazon RDS for MySQL

Question 12

A company needs to ingest data from a relational database into Amazon S3 for analytics. The database is an Amazon RDS MySQL instance. Which AWS service should be used for a one-time historical data load?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is designed for migrating data from relational databases like Amazon RDS MySQL into targets such as Amazon S3, supporting one-time full loads as well as ongoing replication. For a one-time historical data load, DMS can extract the existing data and land it in S3 efficiently. This makes DMS the correct service for the described scenario.

Answer

AWS Glue ETL

Answer

Amazon Athena

Answer

Amazon Kinesis Data Firehose

Question 13

A company wants to use Amazon Redshift Spectrum to query data in Amazon S3. The data is in Parquet format and partitioned by date. Which step is required to enable Redshift Spectrum?

Accepted Answer

Create an external schema and external table in the AWS Glue Data Catalog.. Redshift Spectrum allows querying data directly in Amazon S3 without loading it into Redshift. To use Spectrum, you must define an external schema and external table in the AWS Glue Data Catalog (or an external Hive metastore) that points to the S3 location and specifies the Parquet format and partition structure. This enables Redshift to read the data in place using the Spectrum engine.

Answer

Load the data into Redshift tables using the COPY command.

Answer

Create a separate Redshift Spectrum cluster.

Answer

Copy the data from S3 to Redshift-managed storage.

Question 14

A data engineer is troubleshooting slow query performance on an Amazon Redshift cluster. The cluster has 10 nodes and is using automatic distribution style. The engineer suspects that data distribution is causing excessive data movement. Which steps should the engineer take to diagnose and resolve the issue? (Choose THREE.)

Accepted Answer

Choose appropriate distribution keys for large tables. Choosing appropriate distribution keys for large tables ensures that data is evenly distributed across the cluster slices, minimizing the need for data redistribution during joins and aggregations. Automatic distribution style may not always select the optimal key, leading to excessive data movement and slow query performance.

Answer

Run the VACUUM command to reclaim space

Answer

Increase the number of nodes in the cluster

Question 15

A company wants to ingest data from multiple SaaS applications into Amazon S3 using a fully managed service that supports schema discovery and transformation. Which AWS service should they use?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3. It supports schema discovery and transformation, making it ideal for ingesting data from multiple SaaS apps without writing code.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue

Answer

AWS Data Pipeline

Question 16

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 17

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The database size is 2 TB and the workload is read-heavy. To improve read performance, which option should be used?

Accepted Answer

Create Read Replicas in the same or different regions. Amazon RDS for MySQL Read Replicas offload read traffic from the primary DB instance, directly improving read performance for a read-heavy workload. With a 2 TB database, Read Replicas can be created in the same or different regions, providing horizontal read scaling without impacting the primary instance's write capacity. Multi-AZ deployment already provides high availability but does not improve read performance, as the standby instance is not used for reads.

Answer

Use Amazon ElastiCache to cache database queries

Answer

Increase the instance size to 16xlarge

Answer

Enable Multi-AZ on additional instances

Question 18

A media company is building a data pipeline to ingest user activity logs from multiple sources into Amazon S3. The logs are JSON files generated every minute. The company wants to use Amazon Athena to query the logs with minimal latency and cost. The current approach is to use AWS Kinesis Data Firehose to deliver the logs to S3 with a prefix like 'logs/2024/01/01/00/file.json'. However, when running Athena queries, the team notices high query costs because Athena scans all files in the 'logs/' prefix even when querying for a specific date. What should the team do to reduce the amount of data scanned by Athena?

Accepted Answer

Create a Hive-style partition structure in S3 with keys like 'year=2024/month=01/day=01/hour=00/' and update the Glue Data Catalog accordingly.. Athena reduces data scanned by using partition pruning, which requires a Hive-style partition structure in S3 (e.g., year=2024/month=01/day=01/hour=00/) registered in the Glue Data Catalog. When queries filter on partition columns, Athena only reads the relevant partitions instead of scanning the entire logs/ prefix. This directly addresses the high query cost caused by scanning all files.

Answer

Create an Athena view that filters by date.

Answer

Increase the number of partitions by using a more granular prefix like 'logs/2024/01/01/00/00/'.

Answer

Convert the JSON files to Apache Parquet format using AWS Glue ETL jobs.

Question 19

Which TWO AWS services can be used to automatically back up an Amazon RDS for SQL Server DB instance? (Choose TWO.)

Accepted Answer

Amazon RDS automated backups. Amazon RDS automated backups (C) are a native RDS feature that automatically takes daily snapshots of the DB instance during the backup window and continuously archives transaction logs, enabling point-in-time recovery for RDS for SQL Server. AWS Backup (E) is a fully managed, policy-driven backup service that supports RDS as a protected resource, allowing you to define backup plans with schedules and retention rules that trigger automated RDS snapshots. AWS DMS (A) is a replication and migration service, not a backup mechanism, so it does not create restorable backups. AWS Data Pipeline (B) is an orchestration service for data movement and transformation, not a backup tool for RDS. Amazon S3 (D) is object storage that can hold exported backups, but it does not itself automatically back up an RDS for SQL Server DB instance.

Answer

AWS Database Migration Service (DMS)

Answer

AWS Data Pipeline

Answer

Amazon S3

Question 20

Order the steps to set up a Kinesis Data Analytics application for real-time stream processing.

Accepted Answer

Step 1: Set up source stream; Step 2: Create analytics application; Step 3: Configure application with source and logic; Step 4: Start application; Step 5: Monitor performance. First, set up the source stream. Then create the analytics application, configure it with the source and logic, start it, and finally monitor performance.

Answer

Step 1: Create analytics application; Step 2: Configure application with source and logic; Step 3: Set up source stream; Step 4: Start application; Step 5: Monitor performance

Answer

Step 1: Set up source stream; Step 2: Create analytics application; Step 3: Start application; Step 4: Configure application with source and logic; Step 5: Monitor performance

Answer

Step 1: Set up source stream; Step 2: Create analytics application; Step 3: Configure application with source and logic; Step 4: Monitor performance; Step 5: Start application