DEA-C01 Mock Exam 89 — 25 Questions

Question 1

A data engineer is ingesting streaming data from an IoT fleet into Amazon Kinesis Data Streams. The data must be transformed in real-time and loaded into an Amazon Redshift cluster. Which solution minimizes operational overhead?

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

Accepted Answer

Use Kinesis Data Firehose with a Lambda transformation function. Kinesis Data Firehose is the fully managed service for loading streaming data into Redshift with near-real-time latency. By attaching a Lambda transformation function, you can perform lightweight data transformations (e.g., JSON flattening, field masking) without managing any compute infrastructure. This combination eliminates the need to provision or tune any servers, clusters, or consumer applications, minimizing operational overhead.

Answer

Use AWS Glue ETL jobs running continuously

Answer

Use Kinesis Client Library (KCL) to consume and transform data, then write to Redshift using COPY

Answer

Use AWS Direct Connect to stream data directly into Redshift

Question 2

A company uses Amazon DynamoDB with on-demand capacity for a gaming leaderboard. The table has 100 GB of data and receives 10,000 write requests per second with spikes to 50,000. The application experiences throttling during spikes. Which action should be taken to reduce throttling without changing the application?

Accepted Answer

Switch to provisioned capacity with Auto Scaling. Switching from on-demand to provisioned capacity with Auto Scaling allows you to set a higher minimum and maximum read/write capacity, ensuring that the table can handle spikes up to 50,000 write requests per second without throttling. This change is made at the table level via the AWS console or CLI and does not require any application code modifications. In contrast, enabling DAX (Option D) would require updating the application to use the DAX client, violating the requirement to avoid application changes. Options A and B are ineffective or incompatible: writing data to S3 does not address DynamoDB write throttling, and increasing provisioned read capacity is not applicable for an on-demand table without switching capacity modes first.

Answer

Write data to Amazon S3 and use S3 Select

Answer

Increase the provisioned read capacity units

Answer

Enable DynamoDB Accelerator (DAX)

Question 3

A financial services company is ingesting trade data from multiple exchanges via Amazon Kinesis Data Streams. Each shard receives data from multiple exchanges, and a consumer application (using KCL) processes the data. The company needs to ensure that trades from the same exchange are processed in order. However, the current implementation distributes records to shards using a random partition key, causing trades from the same exchange to be spread across shards and processed out of order. The team must enforce ordering per exchange without significantly reducing throughput. What should the team do?

Accepted Answer

Use the exchange ID as the partition key when putting records into the stream.. Using the exchange ID as the partition key ensures all trades from the same exchange go to the same shard, preserving order. Option A is wrong because increasing shard count would further spread data and break ordering. Option B is wrong because using a single shard would preserve order but reduce throughput due to shard limits. Option D is wrong because implementing a custom sequencer is complex and unnecessary.

Answer

Implement a custom sequence number in the application to reorder after processing.

Answer

Use a single shard for all data to guarantee order.

Answer

Increase the number of shards to 10 per exchange.

Question 4

A company runs a transactional database on Amazon RDS for PostgreSQL with Multi-AZ deployment. The database size is 2 TB and experiences moderate write load. The company recently enabled RDS Performance Insights and noticed a high number of 'TupleLock' wait events during peak hours. The development team reports that a batch update job runs every hour, updating millions of rows in a large table. The job takes longer than expected. The DBA suspects that excessive row-level locking is causing contention. The team wants to minimize lock contention without changing the application code. Which solution should be implemented?

Accepted Answer

Tune the autovacuum settings (e.g., autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) to run more frequently and aggressively.. Tuning autovacuum settings (autovacuum_vacuum_scale_factor and autovacuum_vacuum_threshold) reduces lock contention by cleaning up dead tuples more frequently. In PostgreSQL, row-level locks on heavily updated tables can cause 'TupleLock' wait events. Frequent autovacuum prevents accumulation of dead tuples, reducing the need for lock escalation and shortening update times. Option B (increasing instance size) may improve throughput but does not directly address lock contention. Option C (RDS Proxy) manages connections, not locks. Option D (pg_partman partitioning) reduces row contention but requires application code changes (stem prohibits code changes).

Answer

Increase the RDS instance size to a larger instance class with more vCPUs and memory.

Answer

Enable RDS Proxy to manage database connections and reduce connection overhead.

Answer

Implement table partitioning using the pg_partman extension to split the large table into smaller partitions.

Question 5

A company is using AWS Glue to run ETL jobs that transform data from S3 to Redshift. The jobs are failing intermittently with out-of-memory errors. Which THREE actions can help resolve this issue? (Choose THREE.)

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Increasing the number of DPUs allocated to the Glue job provides more memory and compute resources for the Spark executors, directly addressing out-of-memory errors by allowing larger datasets to be processed without exceeding heap limits. This is a standard scaling approach for memory-intensive ETL workloads in AWS Glue.

Answer

Use S3 Select to filter data before reading into the Glue job

Answer

Use Spark's 'coalesce' function to reduce the number of partitions

Question 6

A company uses Amazon Kinesis Data Analytics (now Managed Service for Apache Flink) to run a Flink application on streaming data. The application fails with 'OutOfMemoryError: Java heap space'. The data volume is 10 MB/s. What is the most likely cause and solution?

Accepted Answer

The application's Parallelism is too low; increase the number of Parallelism and KPUs.. The OutOfMemoryError in a Flink application on Amazon Kinesis Data Analytics (Managed Service for Apache Flink) is most likely due to insufficient parallelism to handle the 10 MB/s data volume. Increasing parallelism distributes the workload across more KPUs (Kinesis Processing Units), reducing memory pressure per operator and preventing heap exhaustion. Option D directly addresses this by scaling resources to match throughput.

Answer

The data contains records larger than 1 MB; split records into smaller chunks.

Answer

Checkpointing is enabled too frequently; reduce checkpoint interval.

Answer

The Flink application is not suitable for 10 MB/s throughput; use Kinesis Data Firehose instead.

Question 7

A company has a 100 TB dataset stored on-premises in a Hadoop cluster. They want to ingest this data into Amazon S3 for processing with AWS Glue. The company has a limited time window and a slow internet connection. Which strategy is MOST appropriate?

Accepted Answer

Use AWS Snowball Edge to physically ship the data to AWS.. AWS Snowball Edge is the most appropriate strategy because the dataset is 100 TB, the time window is limited, and the internet connection is slow. Snowball Edge provides a physical storage device that can be shipped to AWS, bypassing network bandwidth constraints entirely. This approach is designed for large-scale data transfers (typically over 10 TB) where network transfer would be impractical or exceed the available time window.

Answer

Use AWS DataSync over the existing internet connection.

Answer

Use Amazon S3 Transfer Acceleration to speed up the upload.

Answer

Use AWS Direct Connect to establish a high-bandwidth connection.

Question 8

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 9

A data engineer is setting up an Amazon Redshift cluster for a data warehouse. The cluster will store historical sales data and support complex analytical queries. To optimize query performance and manage storage, the engineer needs to choose appropriate distribution styles and sort keys for a large fact table 'sales' and several dimension tables. Which TWO of the following design decisions are BEST practices?

Accepted Answer

Use interleaved sort keys on columns that are frequently used in filter predicates (e.g., date, region, product).. Interleaved sort keys in Amazon Redshift give equal weight to each column in the sort key, making them ideal for queries with filter predicates on multiple columns (e.g., date, region, product). This design optimizes zone maps and minimizes the amount of data scanned, significantly improving query performance for complex analytical workloads on large fact tables.

Answer

Use ALL distribution for the 'sales' fact table to replicate data to every node and avoid data movement.

Answer

Use a compound sort key with the most frequently filtered column first.

Answer

Choose AUTO distribution style for all tables and let Amazon Redshift automatically assign distribution.

Question 10

A company stores sensitive data in Amazon S3 and needs to ensure that data is encrypted at rest. Which AWS service can be used to manage the encryption keys?

Accepted Answer

AWS Key Management Service (KMS). AWS Key Management Service (KMS) is the managed service for creating and controlling encryption keys used to encrypt data at rest in Amazon S3. Option A is correct. Option B (Secrets Manager) is for managing secrets like database passwords, not encryption keys. Option C (IAM) manages access permissions, not encryption keys. Option D (Certificate Manager) handles SSL/TLS certificates, not encryption keys for data at rest.

Answer

AWS Secrets Manager

Answer

AWS Identity and Access Management (IAM)

Answer

AWS Certificate Manager (ACM)

Question 11

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 12

A company is using AWS KMS with customer-managed keys to encrypt data in Amazon RDS. The security team wants to ensure that the key can be rotated automatically every year. Which THREE steps are required to achieve automatic key rotation?

Accepted Answer

Enable automatic key rotation in the KMS key configuration.. Options B, C, and D are correct. To enable automatic rotation for a customer-managed KMS key, you must enable rotation via the KMS console or API (B), ensure the key is a symmetric key (C) as asymmetric keys do not support automatic rotation, and configure the RDS instance to use the key (D) for encryption. Option A is incorrect because rotation can be enabled for existing keys. Option E is incorrect because CloudHSM is not involved.

Answer

Migrate the key to AWS CloudHSM.

Answer

Create a new KMS key and configure RDS to use it.

Question 13

A company uses Kinesis Data Streams to ingest IoT data. The data volume varies, and occasionally the shard write throughput is exceeded, causing ProvisionedThroughputExceeded exceptions. The data engineer needs to handle these spikes without losing data. Which approach is most cost-effective and requires minimal code changes?

Accepted Answer

Use Kinesis Data Firehose as a consumer with retries and buffer settings. Kinesis Data Firehose is the most cost-effective solution because it can be configured as a consumer of the Kinesis Data Stream with built-in retry logic and buffer settings (e.g., buffer size up to 128 MB or buffer interval up to 900 seconds). This handles throughput spikes by buffering data and retrying failed writes without requiring custom code, and it scales automatically without the need to manage shard counts.

Answer

Implement custom retry logic using the Kinesis Client Library with exponential backoff

Answer

Increase the number of shards to handle peak throughput

Answer

Send data to an SQS queue first, then have a Lambda function write to Kinesis

Question 14

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 15

An AWS Glue job that processes streaming data from Amazon Kinesis Data Streams is failing intermittently with 'Failed to checkpoint' errors. The job uses checkpointing to an Amazon S3 bucket every 60 seconds. Which action should the engineer take to resolve the issue?

Accepted Answer

Increase the checkpoint interval to 120 seconds.. The 'Failed to checkpoint' error in AWS Glue streaming jobs typically occurs when the checkpoint operation exceeds the 60-second interval due to high throughput or large state size. Increasing the checkpoint interval to 120 seconds provides more time for the checkpoint to complete, reducing the likelihood of timeouts and allowing the job to stabilize without losing progress.

Answer

Move the checkpoint location to an Amazon DynamoDB table.

Answer

Decrease the Kinesis shard count to reduce throughput.

Answer

Disable checkpointing and rely on Kinesis iterator age.

Question 16

A retail company stores customer transaction data in an Amazon S3 bucket. The data is encrypted using server-side encryption with AWS KMS (SSE-KMS). The company uses an IAM role to allow an Amazon Athena query service to read the data. The data engineer creates a new Athena workgroup and attempts to run a query on the S3 bucket. The query fails with an access denied error. The IAM role has permissions to decrypt the KMS key and read from the bucket. The engineer checks the S3 bucket policy and finds that it does not explicitly allow access. What is the most likely cause of the failure?

Accepted Answer

The S3 bucket policy does not grant the required permissions to the Athena service principal.. The most likely cause is that the S3 bucket policy does not explicitly grant the required permissions to the Athena service principal. Even though the IAM role has permissions to decrypt and read, Athena operates as a service and the bucket policy must allow the `athena.amazonaws.com` principal to perform `s3:GetObject` and `s3:ListBucket` actions. Option B is correct because without this explicit grant, the bucket policy implicitly denies access. Option A is incorrect because the bucket being in a different account would cause an error, but the scenario does not indicate cross-account setup. Option C is incorrect because the IAM role already has decrypt permissions. Option D is incorrect because Athena supports SSE-KMS encrypted data with proper KMS permissions.

Answer

The S3 bucket is in a different AWS account than the Athena workgroup.

Answer

The IAM role does not have permission to use the KMS key for encryption operations.

Answer

Athena does not support querying data encrypted with SSE-KMS.

Question 17

A data engineer is troubleshooting an AWS Lake Formation permissions issue. A user is able to query an Amazon Athena table but cannot see the underlying S3 data in the AWS Glue Data Catalog. The user has been granted SELECT permission on the table in Lake Formation. What is the most likely cause?

Accepted Answer

The user does not have DESCRIBE permission on the table in Lake Formation.. In AWS Lake Formation, the ability to query a table via Athena (which requires SELECT permission) is separate from the ability to view the table's metadata in the Glue Data Catalog. To see the underlying S3 data location or table properties in the catalog, a user needs DESCRIBE permission on the table. Without DESCRIBE, the table appears invisible in the Glue console or API, even though SELECT queries succeed.

Answer

The data location is not registered with Lake Formation.

Answer

The S3 bucket policy does not grant the user access.

Answer

The user does not have the aws:SourceArn condition in the IAM policy.

Question 18

A data engineer runs an AWS Glue ETL job that transforms data in Amazon S3. The job fails with the error shown in the exhibit. Which action will MOST likely fix the issue?

Accepted Answer

Change the worker type from G.1X to G.2X.. The error indicates an out-of-memory error in the Spark executor. This occurs when the executor memory is insufficient for the data being processed. Option D, changing the worker type from G.1X to G.2X, doubles the memory per worker (from 4 GB to 8 GB for Spark executors), addressing the memory issue directly. Option A is incorrect because decreasing the number of workers reduces the total memory and parallelism, worsening the problem. Option B is incorrect because the error is not related to IAM permissions; an IAM policy for S3 write access would resolve a different error (e.g., AccessDenied). Option C is incorrect because increasing the number of workers from 2 to 4 adds more executors but does not increase memory per executor, and the error is likely due to insufficient per-executor memory.

Answer

Decrease the number of workers from 2 to 1.

Answer

Add an IAM policy that grants the Glue job permission to write to S3.

Answer

Increase the number of workers from 2 to 4.

Question 19

A company ingests IoT sensor data into Kinesis Data Streams. The data is then processed by a Lambda function that aggregates readings and writes to DynamoDB. The Lambda function is experiencing high error rates due to throttling. Which TWO actions would reduce throttling?

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 concurrent Lambda invocations and thus lowering the risk of throttling. Option D is correct because switching DynamoDB to on-demand capacity mode eliminates write capacity limits, preventing throttling on the DynamoDB side that can cause Lambda retries and backpressure.

Answer

Increase the number of shards in the Kinesis stream.

Answer

Decrease the batch window in the Lambda event source mapping.

Answer

Increase the Lambda reserved concurrency to 1000.

Question 20

A company uses AWS Glue to transform data stored in S3. The Glue job runs daily and processes data in the range of hundreds of GB. The data engineer wants to optimize the job for cost and performance. Which THREE actions should be taken? (Choose THREE.)

Accepted Answer

Increase the number of DPUs for the job. Increasing the number of DPUs (Data Processing Units) for an AWS Glue job can improve performance by enabling parallel processing of large datasets (hundreds of GB). AWS Glue allocates resources in increments of DPUs, where each DPU provides 4 vCPU and 16 GB of memory; scaling out DPUs reduces execution time, which can lower overall cost if the job runs fewer minutes, balancing cost and performance.

Answer

Store intermediate data in HDFS on Amazon EMR

Answer

Reduce the number of DPUs to save cost