DEA-C01 Practice Exam 3 — 20 Questions

Question 1

A data engineer is designing a data pipeline that ingests data from an on-premises database into Amazon S3 using AWS Database Migration Service (DMS). The data must be encrypted at rest in S3 using SSE-S3. The engineer also needs to track changes to the source database in real time. Which DMS configuration should the engineer use?

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

Accepted Answer

Use DMS with ongoing replication (change data capture) enabled.. DMS with ongoing replication (change data capture) enables real-time tracking of changes from the source database. Option A is incorrect because using a snapshot only captures data at a point in time, not real-time changes. Option C is incorrect because a full load task only loads existing data without capturing ongoing changes. Option D is incorrect because streaming to Amazon Kinesis is unnecessary; DMS CDC can directly replicate changes to S3. Encryption at rest in S3 with SSE-S3 is automatically supported by DMS when writing to S3.

Answer

Use DMS with a snapshot of the source database.

Answer

Use DMS with a full load task only.

Answer

Use DMS with a full load task and then stream to Amazon Kinesis.

Question 2

Refer to the exhibit. An AWS Glue ETL job is failing with an OutOfMemoryError. The job reads from Amazon S3 and performs a GROUP BY on a large dataset. Which change should the data engineer make to resolve this error?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. The OutOfMemoryError in an AWS Glue ETL job performing a GROUP BY on a large dataset indicates that the executors do not have enough memory to handle the shuffle operations required for aggregation. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job increases the total memory and compute resources available, allowing the job to process larger partitions without running out of memory.

Answer

Use coalesce to reduce the number of partitions.

Answer

Increase the number of partitions in the DataFrame.

Answer

Use repartition to increase the number of partitions.

Question 3

A data engineer needs to store semi-structured JSON logs from AWS CloudTrail. The logs are append-only and rarely accessed after 90 days. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is the most cost-effective storage solution for CloudTrail logs that are append-only and rarely accessed after 90 days. It offers the lowest storage cost among AWS options (approximately $0.00099 per GB/month) and is designed for data that is accessed at most once or twice per year, with retrieval times of 12–48 hours. Since the logs are rarely accessed after 90 days, the retrieval latency is acceptable, and the cost savings over S3 Standard (which costs ~$0.023 per GB/month) are substantial.

Answer

Amazon S3 Standard

Answer

Amazon EBS with cold HDD volumes

Answer

Amazon DynamoDB with on-demand capacity

Question 4

A company uses Amazon DynamoDB to store session data for a web application. The application experiences occasional spikes in traffic, causing throttling on the table. The data engineer needs to implement a solution that handles traffic spikes without manual intervention and minimizes cost. What should the data engineer do?

Accepted Answer

Enable DynamoDB Auto Scaling for the table.. DynamoDB Auto Scaling (option D) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, using CloudWatch alarms and the Application Auto Scaling service. This handles traffic spikes without manual intervention and minimizes cost by scaling down during low traffic periods, making it the ideal solution for variable workloads like session data.

Answer

Switch to provisioned capacity with a high fixed read/write capacity.

Answer

Implement DynamoDB Accelerator (DAX) to cache read requests.

Answer

Purchase DynamoDB reserved capacity.

Question 5

A data pipeline uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The delivery occasionally fails with 'Firehose is throttled'. What should be done to reduce throttling?

Accepted Answer

Increase the buffer size and buffer interval. Increasing the buffer size and buffer interval gives Kinesis Data Firehose more time and data volume to accumulate before delivering to S3, reducing the frequency of PutRecord.Batch calls to the underlying Kinesis stream. This directly mitigates throttling by lowering the request rate, as Firehose throttling typically occurs when the per-shard write throughput limit (1,000 records/second or 1 MB/second) is exceeded.

Answer

Enable compression on the Firehose delivery stream

Answer

Decrease the buffer size to flush more frequently

Answer

Increase the number of shards in the Kinesis stream

Question 6

Which TWO options are valid ways to reduce storage costs for an Amazon S3 data lake that stores historical data rarely accessed after 30 days? (Choose TWO.)

Accepted Answer

Create a lifecycle policy to transition objects to S3 Standard-IA after 30 days.. S3 Standard-IA (Infrequent Access) is designed for data accessed less frequently but requires rapid access when needed. Transitioning objects to Standard-IA after 30 days reduces storage costs compared to S3 Standard while maintaining low-latency retrieval, making it ideal for a data lake where historical data is rarely accessed after the first month.

Answer

Enable S3 Transfer Acceleration for all uploads.

Answer

Create a lifecycle policy to delete objects after 30 days.

Answer

Enable S3 Versioning to preserve all object versions.

Question 7

Arrange the steps to implement a data lake on Amazon S3 with AWS Lake Formation.

Accepted Answer

Create S3 bucket, then register it in Lake Formation, then set up administrators, then define schema in Glue Catalog, then grant access to users. Start by creating the S3 bucket. Then register it in Lake Formation, set up administrators, define the schema in Glue Catalog, and finally grant access to users.

Answer

Set up administrators, then create S3 bucket, then register it in Lake Formation, then define schema in Glue Catalog, then grant access to users

Answer

Create S3 bucket, then define schema in Glue Catalog, then register it in Lake Formation, then set up administrators, then grant access to users

Answer

Register S3 bucket in Lake Formation, then create S3 bucket, then set up administrators, then define schema in Glue Catalog, then grant access to users

Question 8

Refer to the exhibit. This log snippet is from a failed AWS Glue job. The job processes a large dataset in memory. What is the MOST likely cause of the OutOfMemoryError?

Accepted Answer

The Glue job is running with insufficient DPUs or worker type.. An OutOfMemoryError in AWS Glue typically occurs when the allocated DPUs or worker type are insufficient for the in-memory processing of a large dataset. Option B is incorrect because unsupported file formats cause parsing errors, not memory errors. Option C is incorrect because mismatched keys in a join cause data skew or incorrect results, but not directly an OutOfMemoryError. Option D is incorrect because too many partitions usually lead to small file overhead, not heap space exhaustion.

Answer

The input data is in an unsupported file format.

Answer

The job is attempting to join two tables with mismatched keys.

Answer

The job has too many partitions.

Question 9

A data engineering team is managing an Amazon Redshift cluster that is used for BI reporting. The cluster has a mix of large tables (some over 1 TB) and many smaller tables. The team notices that queries on a large fact table are slow. The fact table is distributed using KEY distribution on the customer_id column, which has high cardinality. The team wants to improve query performance. They have the option to change the distribution style and sort key. Which redesign should they implement?

Accepted Answer

Change the distribution style to EVEN and set the sort key to a date column used in WHERE clauses.. Using EVEN distribution ensures data is evenly distributed across all nodes, avoiding data skew that can occur with KEY distribution on a high-cardinality column like customer_id. Setting the sort key to a date column used in WHERE clauses enables range-restricted scans, significantly reducing the amount of data scanned for common BI queries that filter by date. This combination improves query performance by maximizing parallelism and minimizing I/O.

Answer

Keep the distribution style as AUTO and set the sort key to customer_id.

Answer

Change the distribution style to ALL and set the sort key to customer_id.

Answer

Change the distribution style to KEY on a different column with high cardinality.

Question 10

Which THREE of the following are benefits of using Amazon DynamoDB Accelerator (DAX)? (Choose three.)

Accepted Answer

Offloads read traffic from the DynamoDB table.. DAX acts as a read-through cache that offloads read traffic from the DynamoDB table, reducing the number of read requests that hit the underlying table and thus lowering the consumed read capacity units (RCUs). This allows the table to handle more concurrent reads without scaling up provisioned capacity.

Answer

Improves write throughput by batching writes.

Answer

Supports write-through caching to improve write performance.

Question 11

Refer to the exhibit. A data engineer has attached this bucket policy to an S3 bucket named data-lake-bucket. The engineer wants to allow only GET requests from the corporate network (10.0.0.0/16) over HTTPS. However, users report that they cannot access objects even when connected to the corporate network. What is the issue?

Accepted Answer

The Deny statement blocks all requests that are not using HTTPS, including those from the corporate network.. The Deny statement with `aws:SecureTransport` set to `false` blocks all HTTP requests. Since the Allow statement only permits GET requests from the corporate network (10.0.0.0/16) but does not require HTTPS, any request from that network that uses HTTP is denied by the explicit Deny. The Deny statement overrides the Allow, so even legitimate corporate users are blocked if they use HTTP.

Answer

The Deny statement should include a condition on the source IP.

Answer

The Allow statement should include a condition for SecureTransport.

Answer

The Allow statement should specify s3:GetObject instead of s3:GetObject.

Question 12

A data engineer needs to transfer 10 TB of data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 100 Mbps, and the transfer must be completed within 48 hours. Which solution meets the requirements?

Accepted Answer

Use AWS Snowball Edge device to transfer data offline. The on-premises Hadoop cluster has 10 TB of data to transfer, but the network bandwidth is only 100 Mbps. At 100 Mbps, the theoretical maximum transfer rate is about 12.5 MB/s, which would take approximately 10 TB / 12.5 MB/s ≈ 800,000 seconds ≈ 222 hours — far exceeding the 48-hour window. AWS Snowball Edge is an offline, physical device that bypasses network constraints entirely, allowing you to transfer the data by shipping the device, which completes within days regardless of bandwidth.

Answer

Use AWS DataSync to transfer data online

Answer

Use S3 Transfer Acceleration over the internet

Answer

Set up AWS Direct Connect to increase bandwidth

Question 13

A data engineer needs to monitor Amazon DynamoDB table metrics to detect throttled requests. Which CloudWatch metric should the engineer set an alarm on?

Accepted Answer

ThrottledRequests. `ThrottledRequests` is the specific Amazon CloudWatch metric that tracks the number of requests to a DynamoDB table that are throttled due to exceeding the provisioned throughput capacity. This metric directly reflects throttling events, making it the appropriate choice for setting an alarm to detect throttled requests.

Answer

ReadThrottleEvents

Answer

SuccessfulRequestLatency

Answer

ConsumedWriteCapacityUnits

Question 14

A data engineer is monitoring a Redshift cluster that is experiencing slow query performance. The cluster has 4 dc2.large nodes. The engineer notices that disk space usage is at 85% across all nodes. Which action would MOST likely improve query performance?

Accepted Answer

Increase the number of nodes to 8.. At 85% disk usage on dc2.large nodes, the cluster is approaching the threshold where Redshift begins to automatically offload data to Amazon S3, causing significant performance degradation due to increased I/O and network latency. Adding more nodes (option C) increases both compute capacity and total disk space, reducing per-node disk pressure and allowing the cluster to keep more data local for faster query execution.

Answer

Change the table design to use DISTKEY and SORTKEY.

Answer

Enable compression on all columns.

Answer

Run the VACUUM command to reclaim space.

Question 15

A company wants to audit all data access events in their S3 buckets, including who accessed objects and from which IP address. Which AWS service should be used to capture these events?

Accepted Answer

AWS CloudTrail with data events enabled. AWS CloudTrail can log S3 API calls such as GetObject, PutObject, and ListObjects when data events are enabled, capturing details like who accessed an object and from which IP address. Option D is incorrect because S3 Server Access Logs provide object-level logs but do not include IAM user or role details. Option B is incorrect because Amazon CloudWatch Logs can store and monitor logs but does not capture S3 data events directly. Option C is incorrect because AWS Config records resource configuration changes, not data access events.

Answer

Amazon CloudWatch Logs

Answer

AWS Config

Answer

Amazon S3 Server Access Logs

Question 16

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 the correct choice for a one-time historical data load from Amazon RDS MySQL to Amazon S3. DMS supports full-load migrations from relational databases to S3, making it ideal for this use case. AWS Glue ETL can also perform similar tasks but is more suited for complex transformations and scheduled jobs, and DMS is the dedicated service for database migrations. Amazon Athena is a query service, not an ingestion tool. Amazon Kinesis Data Firehose is designed for streaming data, not one-time loads.

Answer

AWS Glue ETL

Answer

Amazon Athena

Answer

Amazon Kinesis Data Firehose

Question 17

A company uses AWS Lake Formation to manage data lake permissions. A data analyst cannot query a table in Athena, although the table appears in the catalog. The analyst has IAM permissions to run Athena. What is the MOST likely cause?

Accepted Answer

The analyst lacks Lake Formation permissions on the table.. Lake Formation permissions are separate from IAM; even if the analyst has IAM permissions to run Athena, they also need specific Lake Formation permissions (e.g., SELECT) on the table to query it. The table appears in the catalog because the analyst has DESCRIBE permission on the database or table, but querying requires additional data access permissions. Option A is incorrect because the table appears in the catalog, so it is registered. Option B is incorrect because the S3 bucket policy might allow access, but Lake Formation can override it. Option D is incorrect because the workgroup output location affects where query results are stored, not the ability to query a specific table.

Answer

The Glue Data Catalog does not have the table registered.

Answer

The S3 bucket policy denies access to the analyst's IAM role.

Answer

The Athena workgroup is not configured with the correct output location.

Question 18

A data engineer needs to transform CSV files in S3 to Parquet format using a serverless solution. The files are large (up to 5 GB each) and arrive irregularly. Which TWO services can accomplish this with minimal operational overhead? (Choose TWO.)

Accepted Answer

AWS Glue ETL job. AWS Glue ETL job is correct because it is a fully managed, serverless service that can automatically convert CSV to Parquet without provisioning infrastructure. It handles large files (up to 5 GB) by scaling Spark executors dynamically, and can be triggered by S3 events for irregular arrivals, minimizing operational overhead.

Answer

Amazon EC2 with a script

Answer

Amazon EMR cluster

Answer

Amazon Redshift Spectrum

Question 19

A data engineering team is ingesting data from multiple sources into Amazon S3 using AWS Glue ETL jobs. The jobs are failing intermittently with the error: 'Task ran out of memory'. The input data size varies widely from 100 MB to 10 GB per job. Which configuration change would best mitigate this issue?

Accepted Answer

Enable job bookmarking to process only incremental data. Enabling job bookmarking allows the Glue ETL job to process only incremental (new or changed) data, which directly addresses the intermittent out-of-memory errors caused by widely varying input sizes (100 MB to 10 GB). By skipping previously processed data, the job consistently handles smaller data volumes per run, reducing memory pressure on the Spark executors.

Answer

Increase the number of workers in the Glue job

Answer

Reduce the batch size in the S3 source node

Answer

Change the job type from Spark to Python shell

Question 20

A company is streaming data from an application to Amazon Kinesis Data Streams. The data must be transformed in real time and then stored in Amazon S3 in Parquet format. Which AWS service should be used for the transformation step?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink.. Amazon Kinesis Data Analytics for Apache Flink is a serverless service that can run Apache Flink applications to perform real-time transformations on streaming data. Option A (Amazon Kinesis Data Firehose with a Lambda transformation) is primarily for loading data and has limitations for complex transformations. Option B (Amazon EMR running Apache Spark Streaming) can work but adds management overhead and is not the simplest managed service. Option D (AWS Lambda with a Kinesis trigger) is suitable for lightweight transformations but may hit execution time limits for complex or long-running transformations.

Answer

Amazon Kinesis Data Firehose with a Lambda transformation.

Answer

Amazon EMR running Apache Spark Streaming.

Answer

AWS Lambda with a Kinesis trigger.