DEA-C01 Mock Exam 83 — 25 Questions

Question 1

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?

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

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 2

A data engineering team is using Amazon EMR to process large datasets stored in Amazon S3. The cluster uses Spot Instances for cost savings. During processing, the team notices that tasks are failing due to Spot Instance interruptions. The team needs to make the EMR job resilient to Spot interruptions without increasing costs significantly. Which solution should they implement?

Accepted Answer

Use EMR instance fleets with a mix of Spot and On-Demand, setting the allocation strategy to 'diversified' and using On-Demand for core nodes.. Using EMR instance fleets with a mix of Spot and On-Demand, setting the allocation strategy to 'diversified', and using On-Demand for core nodes ensures resilience to Spot interruptions without significant cost increase. On-Demand core nodes provide stability for HDFS and critical processing, while diversified allocation for Spot task nodes reduces the risk of simultaneous interruptions. Option A is incorrect because the 'lowest price' allocation strategy prioritizes the cheapest Spot instances, which often have higher interruption rates, and does not protect core nodes. Option B is incorrect because increasing core nodes with On-Demand instances raises costs significantly. Option C is incorrect because using only Spot Instances with automatic termination and checkpointing does not prevent job failures during interruptions; automatic termination would terminate the job, and checkpointing only helps with recovery, not resilience.

Answer

Use EMR instance fleets with a mix of Spot and On-Demand, but set the allocation strategy to 'lowest price'.

Answer

Increase the number of core nodes using On-Demand instances.

Answer

Use only Spot Instances but enable automatic termination and checkpointing.

Question 3

A data engineer needs to store semi-structured JSON data that is accessed infrequently but requires immediate retrieval when needed. The data must be durable and cost-effective. Which Amazon S3 storage class should be used?

Accepted Answer

S3 Standard-IA. S3 Standard-IA is the correct choice because it offers the same durability and low-latency retrieval as S3 Standard but at a lower storage cost, making it ideal for infrequently accessed data that still needs immediate retrieval when requested. The scenario specifies 'infrequently accessed' and 'immediate retrieval,' which aligns with Standard-IA's design for data accessed less than once a month but with millisecond first-byte latency.

Answer

S3 Glacier

Answer

S3 Standard

Answer

S3 One Zone-IA

Question 4

A company has an Amazon S3 bucket with versioning enabled. They want to automatically delete noncurrent versions of objects after 30 days. Which lifecycle rule action should be used?

Accepted Answer

NoncurrentVersionExpiration. The NoncurrentVersionExpiration lifecycle action is specifically designed to remove noncurrent object versions after a specified number of days. Since versioning is enabled and the requirement is to delete noncurrent versions after 30 days, this action directly meets the goal without affecting current versions or other lifecycle aspects.

Answer

Expiration

Answer

NoncurrentVersionTransition

Answer

AbortIncompleteMultipartUpload

Question 5

A data engineer needs to ingest JSON files from an S3 bucket into a DynamoDB table. The files are updated hourly and contain new records. Which AWS service should be used to trigger a Lambda function for each new object?

Accepted Answer

S3 Event Notifications. S3 Event Notifications are the correct choice because they are designed to trigger AWS Lambda functions directly in response to object creation events (e.g., `s3:ObjectCreated:*`) in an S3 bucket. This allows the data engineer to automatically invoke a Lambda function for each new JSON file as it is uploaded, enabling ingestion into DynamoDB without polling or additional infrastructure.

Answer

Kinesis Data Firehose

Answer

Amazon EventBridge

Answer

Amazon SQS

Question 6

A data engineer is designing a data lake on Amazon S3. The data is frequently accessed by multiple analytics services, and the company needs to enforce fine-grained access control based on data tags. Which combination of AWS services should be used?

Accepted Answer

AWS Lake Formation with tag-based access control. AWS Lake Formation with tag-based access control (TBAC) is the correct choice because it provides fine-grained, attribute-based access control (ABAC) at the column, row, and cell level across a data lake on S3. By assigning LF-tags to Data Catalog resources and defining permissions based on those tags, you can enforce granular access policies that scale without managing individual user-to-resource mappings. This directly meets the requirement for tag-driven, fine-grained access for multiple analytics services.

Answer

S3 Block Public Access settings

Answer

S3 Access Points with bucket policies

Answer

S3 Object Lambda with IAM policies

Question 7

A data engineer is designing a data lake on Amazon S3. Which feature should be used to manage the lifecycle of objects and move them to cheaper storage classes automatically?

Accepted Answer

S3 Lifecycle policies. S3 Lifecycle policies are the correct feature for automatically managing object lifecycles and transitioning objects to cheaper storage classes (e.g., from S3 Standard to S3 Glacier Deep Archive) based on age or other rules. This directly meets the requirement to move objects to cost-optimized storage without manual intervention.

Answer

S3 Object Lock

Answer

S3 Storage Class Analysis

Answer

S3 Inventory

Question 8

A company is migrating an on-premises Apache Hadoop cluster to Amazon EMR. The data is stored in HDFS and must be moved to Amazon S3. Which THREE considerations are important when designing the migration? (Choose THREE.)

Accepted Answer

EMR can read data directly from S3 using EMRFS. Amazon EMR uses the EMR File System (EMRFS) to directly read and write data stored in Amazon S3, treating S3 as a scalable, durable data lake without needing to first copy data into HDFS. This allows EMR clusters to process data directly from S3, enabling decoupled compute and storage.

Answer

S3 supports POSIX file system semantics

Answer

HDFS can be directly mounted as an S3 bucket

Question 9

A data engineer is setting up an Amazon RDS for MySQL database. The database must be highly available and automatically failover in case of an AZ outage. Which TWO configurations should the engineer enable? (Choose TWO.)

Accepted Answer

Multi-AZ deployment. Multi-AZ deployment (Option A) automatically provisions and maintains a synchronous standby replica in a different Availability Zone (AZ). In the event of an AZ outage, Amazon RDS automatically fails over to the standby, ensuring high availability with minimal downtime. This is the core mechanism for automatic failover in RDS for MySQL.

Answer

Enhanced Monitoring

Answer

Automated backups with a retention period of 30 days

Answer

Read replicas in a different Region

Question 10

A data engineer needs to store streaming data from multiple sources into Amazon S3. The data should be organized by source, date, and hour. The engineer wants to minimize processing overhead. Which THREE S3 features should the engineer use to achieve this? (Choose THREE.)

Accepted Answer

S3 Inventory to list objects and their metadata.. S3 prefixes organize objects into a hierarchy. S3 Inventory provides a list of objects. S3 Event Notifications trigger downstream processing. Batch Operations are for bulk actions, not organization. Object Lock is for retention. S3 Select is for querying within files.

Answer

S3 Object Lock to prevent object modifications.

Answer

S3 Batch Operations to rename objects after upload.

Question 11

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 12

Which TWO actions can reduce the cost of an Amazon S3 bucket that stores infrequently accessed data? (Choose 2.)

Accepted Answer

Use lifecycle policies to expire objects after a certain period. Lifecycle policies allow you to define rules that automatically expire (delete) objects after a specified period, which directly reduces storage costs by removing data that is no longer needed. For infrequently accessed data, deleting obsolete objects prevents paying for unnecessary storage over time.

Answer

Enable cross-region replication

Answer

Enable versioning to keep multiple versions

Answer

Enable MFA Delete for extra security

Question 13

A company uses S3 to store sensitive customer data. To prevent accidental public access, a data engineer needs to ensure that all S3 buckets block public access at the account level. Which AWS service should be used to enforce this policy?

Accepted Answer

Use an SCP in AWS Organizations to deny s3:PutBucketPublicAccessBlock. AWS Organizations with SCPs can centrally control permissions across all accounts, including blocking public access to S3 buckets. Option A is wrong because IAM policies are per-identity and not account-wide. Option B is wrong because S3 Block Public Access settings exist per bucket or account, but to enforce across all accounts, Organizations is needed. Option D is wrong because AWS Config can detect non-compliance but not enforce. Option C is correct.

Answer

Enable S3 Block Public Access at the account level in the management account

Answer

Create an IAM policy that denies s3:PutBucketPolicy

Answer

Set up AWS Config rules to automatically remediate public buckets

Question 14

A data engineer is building a streaming pipeline using Amazon Kinesis Data Streams and AWS Lambda. The Lambda function processes records and writes to Amazon DynamoDB. The engineer notices that the Lambda function is throttled during high traffic. Which action should the engineer take to reduce throttling?

Accepted Answer

Increase the number of shards in the Kinesis data stream. Increasing the number of shards in the Kinesis data stream increases the overall throughput of the stream, which allows the Lambda event source mapping to poll more shards concurrently. Each shard is processed by one Lambda invocation at a time, so more shards mean more concurrent Lambda executions, reducing the per-invocation load and the likelihood of throttling.

Answer

Increase the Lambda function timeout

Answer

Disable retries on the Lambda function

Answer

Use an Amazon SQS queue as an intermediate buffer

Question 15

A company uses Amazon DynamoDB with on-demand capacity for a gaming application that experiences unpredictable traffic spikes. The application reads the same set of 'hot' items frequently. Users report high latency during peak hours. Which action would MOST effectively reduce read latency for the hot items?

Accepted Answer

Enable DynamoDB Accelerator (DAX) for the table.. DynamoDB Accelerator (DAX) is an in-memory cache that sits between the application and DynamoDB, providing microsecond read latency for frequently accessed items. Since the application reads the same set of 'hot' items repeatedly, DAX can serve these reads from its cache, bypassing the storage layer and reducing latency during traffic spikes without requiring any table schema changes.

Answer

Switch to provisioned capacity with auto-scaling.

Answer

Increase the read capacity units for the table.

Answer

Enable DynamoDB Global Tables for multi-region replication.

Question 16

A company is using Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an Amazon S3 bucket. The team notices that the application is experiencing high latency during peak hours. The stream has 8 shards, and the application is configured with a parallelism of 4. Which action would most likely reduce the latency?

Accepted Answer

Increase the parallelism of the Flink application to 8.. The application has 8 shards but only a parallelism of 4, meaning each Flink subtask must process data from 2 shards. This creates a bottleneck because a single subtask cannot process data from multiple shards faster than the slowest shard's throughput. Increasing parallelism to 8 matches the shard count, allowing each subtask to read from exactly one shard, eliminating the contention and reducing latency.

Answer

Decrease the batch size in the S3 sink.

Answer

Use a larger Kinesis Data Analytics application instance type.

Answer

Increase the checkpointing interval to reduce overhead.

Question 17

A data pipeline uses Amazon Kinesis Data Firehose to deliver data to an S3 bucket. The delivery stream is configured with a buffer interval of 60 seconds and a buffer size of 5 MB. The data arrives at an average rate of 2 MB per second. What is the expected time interval between S3 writes?

Accepted Answer

Approximately 2.5 seconds. Amazon Kinesis Data Firehose writes to S3 when either the buffer interval (60 seconds) or buffer size (5 MB) is reached first. With data arriving at 2 MB/s, the 5 MB buffer fills in 2.5 seconds (5 MB / 2 MB/s), triggering a write before the 60-second interval expires. Thus, the expected time between S3 writes is approximately 2.5 seconds.

Answer

Approximately 30 seconds

Answer

Approximately 60 seconds

Answer

Approximately 10 seconds

Question 18

A company uses AWS Glue ETL to process data from Amazon S3 and write results to Amazon Redshift. The job fails with a memory error when processing large files. Which action should the data engineer take to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job provides more memory and compute resources, which directly addresses the out-of-memory error when processing large files. Glue jobs run on Apache Spark, and insufficient DPUs can cause executors to run out of memory during shuffle or aggregation operations on large datasets.

Answer

Reduce the number of partitions in the Glue job.

Answer

Switch to a smaller instance type in the Glue job configuration.

Answer

Use S3 Select to filter columns before reading into Glue.

Question 19

A company is using Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed from JSON to Parquet format before landing in S3. The transformation logic is simple: convert the JSON schema to Parquet. Which approach meets the requirements with the least operational overhead?

Accepted Answer

Use the built-in data format conversion feature of Firehose with an AWS Glue Data Catalog table. Amazon Kinesis Data Firehose provides a built-in data format conversion feature that can automatically convert incoming JSON data to Parquet format using an AWS Glue Data Catalog table as the schema reference. This approach requires no custom code, no additional infrastructure, and no manual transformation logic, making it the simplest solution with the least operational overhead for a straightforward JSON-to-Parquet conversion.

Answer

Use an AWS Lambda function to transform records to Parquet before sending to Firehose

Answer

Use Amazon Kinesis Data Analytics to convert the stream to Parquet

Answer

Provision an Amazon EMR cluster to convert the data in micro-batches

Question 20

A company is using Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The data is delivered in 5-minute intervals. The company wants to reduce the delivery frequency to 1 minute to get data faster. Which parameter should be changed in the Firehose delivery stream configuration?

Accepted Answer

Reduce the buffer interval from 300 seconds to 60 seconds.. The buffer interval determines how often data is delivered to the destination. Changing it from 300 seconds to 60 seconds will deliver data every minute. Option B (buffer size) affects delivery based on data volume, not time. Option C (compression) does not affect frequency. Option D (partitioning) does not affect delivery frequency.

Answer

Increase the buffer size to trigger delivery sooner.

Answer

Enable dynamic partitioning to deliver data more frequently.

Answer

Enable compression to reduce data size and speed up delivery.