DEA-C01 Practice Exam 56 — 20 Questions

Question 1

Refer to the exhibit. A data engineer is configuring a Kinesis Data Firehose delivery stream. The stream is expected to receive bursts of 10 MB of data every 2 minutes. What is the maximum time it will take for data to be delivered to S3 during a burst?

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

Accepted Answer

300 seconds. The Kinesis Data Firehose delivery stream is configured with a buffer interval of 300 seconds (5 minutes) and a buffer size of 5 MB. During a burst of 10 MB every 2 minutes, the buffer will fill to 5 MB in 1 minute, so the buffer will be flushed every minute due to reaching the size threshold. However, the question asks for the maximum time it will take for data to be delivered to S3 during a burst. The maximum time is determined by the buffer interval, which is 300 seconds. Even though the size trigger may cause earlier flushes, the maximum possible time before delivery is the interval setting of 300 seconds. Therefore, the correct answer is 300 seconds (option A).

Answer

60 seconds

Answer

1 second

Answer

600 seconds

Question 2

A data engineer needs to transform JSON data into CSV format using AWS Glue. The transformation is simple and must be executed on a schedule. Which Glue component is MOST suitable?

Accepted Answer

Glue ETL job. A Glue ETL job is the most suitable component because it can execute a script (Python/Scala) to transform JSON data into CSV format and can be scheduled to run on a recurring basis. Glue Crawlers only discover and catalog metadata, not transform data. The Glue Data Catalog is a metadata repository, not a transformation tool. A Glue Development Endpoint is used for interactive development and testing, not for scheduled production jobs.

Answer

Glue Crawler

Answer

Glue Data Catalog

Answer

Glue Development Endpoint

Question 3

A company uses Amazon Kinesis Data Firehose to ingest data into an S3 bucket. The data is in JSON format and the team wants to convert it to Parquet before storage. Which TWO configurations are required?

Accepted Answer

Create a Glue table with the schema of the data.. Amazon Kinesis Data Firehose can automatically convert JSON data to Parquet format before delivery to S3. To enable this, you must: (1) Enable data format conversion in the Firehose delivery stream settings and set the output format to Parquet (Option E). (2) Provide a schema for the data, which is done by referencing an AWS Glue table that defines the schema (Option B). Option A is incorrect because Kinesis Data Analytics is not required; Firehose handles conversion natively. Option C (Lambda) is a valid way to transform data but is not required and not listed as a configuration for this purpose. Option D (Athena) is for querying data already in S3, not for conversion during ingestion.

Answer

Use Kinesis Data Analytics to transform data to Parquet.

Answer

Configure a Lambda function to convert data on the fly.

Answer

Set up an Athena table to read the data.

Question 4

A data engineer needs to ingest data from an Amazon RDS for PostgreSQL database into Amazon S3 on a daily basis. The data volume is approximately 500 GB per day. Which service is most appropriate for this task?

Accepted Answer

AWS Glue with a scheduled ETL job. AWS Glue with a scheduled ETL job is the most appropriate choice because it provides a fully managed, serverless ETL service that can efficiently extract 500 GB of data daily from Amazon RDS for PostgreSQL and write it to Amazon S3. Glue can handle large volumes via its distributed Spark-based execution, and scheduling ensures the daily cadence without manual intervention.

Answer

AWS Database Migration Service (DMS) with continuous replication

Answer

Amazon Athena with federated query to RDS

Answer

Amazon EMR with Spark job

Question 5

A data engineer is troubleshooting a step function that orchestrates ETL jobs. The state machine fails with 'State Machine Execution Throttled' error. What should the engineer do to resolve this?

Accepted Answer

Request a service quota increase for concurrent executions.. The 'State Machine Execution Throttled' error indicates that the state machine has exceeded the default concurrent execution limit for Step Functions. The proper resolution is to request a service quota increase from AWS Support for the concurrently running executions. Option A is incorrect: reducing the number of steps does not affect the limit on concurrent executions. Option B is incorrect: a CloudWatch alarm can detect throttling but cannot resolve it; the alarm would only notify, not increase quotas. Option C is incorrect: API rate limits are not the same as execution throttling; Step Functions has separate limits for API call rate and concurrent executions. Adjusting API rate limits would not address execution throttling.

Answer

Reduce the number of steps in the state machine.

Answer

Set up a CloudWatch alarm to detect throttling and retry.

Answer

Adjust the API rate limits in the state machine definition.

Question 6

A data engineer has set up an AWS Lambda function that processes files uploaded to an S3 bucket. The function is triggered by S3 event notifications. However, the function is not being invoked when a file is uploaded. The engineer checks the Lambda function's CloudWatch Logs and finds no execution logs. What should the engineer check FIRST?

Accepted Answer

Verify that the S3 bucket has an event notification configured for the Lambda function.. The correct first step is to verify the S3 event notification configuration (Option C). Since there are no execution logs in CloudWatch, the Lambda function is not being triggered at all. This indicates a problem with the trigger mechanism, not with the function's code, permissions, or network configuration. Option A (checking code) is premature because code errors would appear in logs after invocation. Option B (IAM role) affects what the function can do during execution, not whether it is invoked. Option D (VPC) affects network access but does not prevent triggering. Therefore, the S3 bucket's event notification must be checked to ensure it is correctly set up to invoke the Lambda function.

Answer

Check the Lambda function's code for errors.

Answer

Verify that the Lambda function's IAM role has permissions to read from S3.

Answer

Check if the Lambda function is attached to a VPC.

Question 7

Refer to the exhibit. A data engineer is configuring a Kinesis Data Firehose delivery stream. The stream is expected to receive bursts of 10 MB of data every 2 minutes. What is the maximum time it will take for data to be delivered to S3 during a burst?

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

Accepted Answer

The IAM policy is missing the s3:AbortMultipartUpload and s3:ListBucket actions.. Kinesis Data Firehose requires the s3:AbortMultipartUpload and s3:ListBucket permissions in addition to s3:PutObject to successfully write data to an S3 bucket. The IAM policy shown only grants s3:PutObject and s3:GetObject, so without these missing actions, the delivery stream fails with access denied. Option A is incorrect because enabling server-side encryption on the S3 bucket does not inherently cause access denial if the IAM role has the necessary permissions; the issue here is the missing S3 actions. Option B is incorrect because the policy does not involve KMS keys, and the error is not related to encryption key access. Option C is incorrect because there is no indication that the bucket policy explicitly denies the Firehose service principal; the problem is the IAM role's insufficient permissions.

Answer

The S3 bucket has server-side encryption enabled that needs additional permissions.

Answer

The IAM role does not have permission to use AWS KMS keys.

Answer

The bucket policy denies access from the Firehose service principal.

Question 8

A data engineer is ingesting data from an on-premises database to Amazon S3 using AWS DataSync. The data transfer is scheduled to run daily at midnight. The engineer notices that the transfer takes longer than expected and sometimes does not complete before the next scheduled task. What should the engineer do to ensure the transfer completes within the window?

Accepted Answer

Increase the bandwidth limit in the DataSync task settings.. Increase the bandwidth limit in the DataSync task settings. Increasing the bandwidth limit allows DataSync to use more available network capacity, thereby speeding up the data transfer and ensuring it completes within the scheduled window. Option B is incorrect because decreasing the bandwidth limit would slow the transfer further. Option C is incorrect because increasing the schedule frequency does not address the underlying bandwidth issue; it would merely start more transfers that would still be slow. Option D is incorrect because S3 Transfer Acceleration is designed for long-distance transfers over public internet and may not help if the bottleneck is on-premises bandwidth; additionally, DataSync is the recommended service for this scenario.

Answer

Decrease the bandwidth limit to reduce network congestion.

Answer

Increase the schedule frequency to every 12 hours.

Answer

Use S3 Transfer Acceleration instead of DataSync.

Question 9

A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The data contains nested fields. Which Glue feature should the engineer use to define the schema and handle the nested structure?

Accepted Answer

Use the 'Relationalize' transform in a Glue ETL script.. The 'Relationalize' transform in AWS Glue is specifically designed to convert nested JSON data into flat tables by extracting and flattening nested fields into separate relational tables. This allows handling complex nested structures effectively. Option A is incorrect because 'FindMatches' is used for deduplication, not for handling nested data. Option B is incorrect because 'DropFields' is used to remove fields from a dataset, not to transform nested structures. Option D is incorrect because 'Spigot' is used for writing sample data for testing or debugging, not for schema definition or handling nesting.

Answer

Use the 'FindMatches' transform to identify duplicates.

Answer

Use the 'DropFields' transform to remove nested fields.

Answer

Use the 'Spigot' transform to write sample data.

Question 10

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time clickstream data. The application reads from a Kinesis stream and writes aggregated results to an Amazon S3 bucket. The company notices that the application is falling behind and the checkpoint duration is increasing. Which THREE actions should the data engineer take to improve performance? (Choose THREE.)

Accepted Answer

Use multiple S3 prefixes in the output path to avoid throttling.. Options B, D, and E are correct. Using multiple S3 prefixes in the output path (B) reduces the risk of S3 write throttling by distributing writes across multiple partition keys. Increasing the checkpoint interval (D) reduces the frequency of checkpointing, thus decreasing the overhead and allowing the application to process more data between checkpoints. Increasing parallelism (E) allows the Flink application to process more data in parallel, improving throughput. Decreasing the number of shards (A) would reduce the incoming data rate and potentially worsen the lag. Increasing heap memory (C) might help with memory pressure but does not directly address checkpoint duration or processing lag; the primary issues are related to parallelism and checkpoint overhead.

Answer

Decrease the number of shards in the source Kinesis stream.

Answer

Increase the heap memory of the Flink application.

Question 11

A data engineer is troubleshooting an AWS Glue crawler that is not correctly inferring the schema of CSV files stored in Amazon S3. The files have headers, but the crawler is treating the header row as data. The crawler is configured with a custom classifier that has a CSV classifier with 'Column header' set to 'Use first row as header'. What is the most likely reason the crawler is not recognizing the header?

Accepted Answer

The CSV classifier's 'Quote symbol' setting does not match the files.. The CSV classifier's 'Quote symbol' setting must match the files. If the files do not use quotes, but the classifier expects a quote symbol (e.g., double quotes), the classifier may fail to match, causing the crawler to fall back to default behavior and treat the header row as data. Option B is wrong because a varying number of columns would cause schema issues, not header misrecognition. Option C is wrong because the delimiter is unrelated to header detection; a custom delimiter can be set separately. Option D is wrong because case of header text does not affect the crawler's ability to recognize headers.

Answer

The CSV files have a varying number of columns across rows.

Answer

The CSV files have a different delimiter than the default comma.

Answer

The header row contains uppercase letters.

Question 12

A company uses AWS Lake Formation to manage data lake permissions. A data engineer needs to grant an IAM role 'Read' access to a specific database and all its tables in the Data Catalog. What is the MOST efficient way to achieve this?

Accepted Answer

Grant 'Select' on the database and select 'Include' to apply to all tables. Lake Formation allows granting 'Select' permission on a database with the 'Include' option, which automatically applies the same permission to all current and future tables in that database. This is the most efficient way to grant read access to an IAM role for a specific database and all its tables in the Data Catalog, as it avoids manual per-table grants.

Answer

Grant 'Super' permission on the Data Catalog

Answer

Add the IAM role to the Lake Formation administrators group

Answer

Grant 'Describe' on the database and 'Select' on each table individually

Question 13

A data engineer is designing a data pipeline that ingests data from multiple sources into Amazon S3, then processes it with AWS Glue and loads it into Amazon Redshift. Which THREE practices should be implemented to ensure data quality?

Accepted Answer

Implement data validation checks at the ingestion stage. Implementing data validation checks at the ingestion stage ensures that only valid data enters the pipeline, catching issues early. Option B is correct because AWS Glue DataBrew provides data profiling and schema enforcement capabilities that help maintain data quality by identifying anomalies and enforcing schemas. Option E is correct because setting up Amazon CloudWatch alarms allows proactive monitoring of pipeline failures and data anomalies, enabling timely responses. Option C is incorrect because compressing data files reduces storage costs but does not directly address data quality. Option D is incorrect because manual sampling is not scalable and cannot consistently ensure data quality in large-scale pipelines.

Answer

Compress data files to reduce storage costs

Answer

Use manual sampling to check data quality periodically

Question 14

A team uses Amazon Kinesis Data Analytics to process streaming data. They notice that the application's output is delayed. Which AWS service can be used to monitor the application's performance and identify bottlenecks?

Accepted Answer

Amazon CloudWatch. (Amazon CloudWatch) is correct because CloudWatch provides metrics and logs for monitoring Kinesis Data Analytics application performance, such as CPU utilization, memory usage, and throughput, helping to identify bottlenecks causing output delays. Option A (AWS CloudTrail) is used for auditing API calls, not performance monitoring. Option C (Amazon Athena) is an interactive query service for analyzing data in S3, not for monitoring real-time streaming applications. Option D (AWS X-Ray) traces requests through applications but is not the primary tool for monitoring Kinesis Data Analytics performance.

Answer

AWS CloudTrail

Answer

Amazon Athena

Answer

AWS X-Ray

Question 15

A healthcare company is building a data pipeline to ingest electronic health records (EHR) from hospitals. The data is sent as JSON files via SFTP to an on-premises server. The company wants to move this data to AWS using AWS Transfer Family (SFTP) and then process it with AWS Glue. Data sovereignty regulations require that all data remain within the EU (Frankfurt) region. The pipeline must detect when a new file arrives and start the Glue job automatically. The engineer has set up an AWS Transfer Family server in Frankfurt, and files are uploaded to an S3 bucket in the same region. However, the Glue job is not triggering automatically. The engineer needs to implement automated triggering. What should the engineer do?

Accepted Answer

Set up an S3 event notification on the bucket to invoke an AWS Lambda function that starts the Glue job.. S3 event notifications can be configured to invoke an AWS Lambda function when a new object is created in the bucket, and the Lambda function can then start the AWS Glue job. This is the simplest and most direct method for automated triggering. Option A (Step Functions) adds unnecessary complexity and polling overhead. Option B (CloudWatch Events) cannot directly monitor S3 object creation events; it would require additional custom logic. Option C (SQS) is an unnecessary intermediary; S3 can directly invoke Lambda without needing a queue.

Answer

Configure AWS Step Functions to poll the S3 bucket every minute and start the Glue job if new files exist.

Answer

Configure Amazon CloudWatch Events to trigger the Glue job on a schedule that checks for new files.

Answer

Use Amazon Simple Queue Service (SQS) to queue file metadata and have a Lambda function poll the queue to start the Glue job.

Question 16

A data engineer needs to monitor the number of records processed by an Amazon Kinesis Data Analytics application and trigger an alarm if the count drops below a threshold over 5 minutes. Which CloudWatch metric should be used?

Accepted Answer

millisBehindLatest (from KinesisDataAnalytics). 'millisBehindLatest' is a metric published by KinesisDataAnalytics that indicates how far behind the application is in processing records. Monitoring this metric allows the engineer to set an alarm when the number of records processed drops, as a high millisBehindLatest value indicates the application is falling behind. Option B (IncomingRecords) is from Kinesis Streams, not KinesisDataAnalytics. Option C (DPUCount) is a Glue metric. Option D (IncomingBytes) is a Kinesis Firehose metric.

Answer

IncomingRecords (from Kinesis Streams)

Answer

DPUCount (from Glue)

Answer

IncomingBytes (from Kinesis Firehose)

Question 17

A data engineer runs an AWS Glue crawler that is configured to crawl an S3 bucket named 'my-data-lake' and update the Glue Data Catalog. The crawler fails with an access denied error. The IAM role attached to the crawler has the policy shown in the exhibit. What is the likely cause of the failure?

Accepted Answer

The policy does not allow logging to CloudWatch Logs.. AWS Glue crawlers require permissions to write logs to CloudWatch Logs for monitoring and debugging. Without the `logs:CreateLogGroup`, `logs:CreateLogStream`, and `logs:PutLogEvents` actions, the crawler fails with an access denied error even if it has S3 and Glue Data Catalog permissions. The IAM policy shown does not include these CloudWatch Logs permissions, making option C the correct answer.

Answer

The policy does not allow glue:CreateTable on the 'my-data-lake' database.

Answer

The policy does not allow s3:PutObject on the 'my-data-lake' bucket.

Answer

The policy does not allow s3:ListBucket on the 'my-data-lake' bucket.

Question 18

A data engineer is setting up a data pipeline to ingest data from an Amazon RDS for MySQL database into Amazon S3 using AWS Glue ETL. The Glue job uses a JDBC connection to read from the MySQL database. The job runs successfully, but the engineer notices that the job is taking longer than expected. The MySQL database is 500 GB in size and the Glue job uses 10 workers of type G.1X. The engineer wants to improve the performance of the extraction phase. The database is actively used by other applications, so the engineer must minimize the impact on the source database. Which approach should the engineer take?

Accepted Answer

Partition the table by a numeric column, such as the primary key, and use the 'hashex' or 'hashpar' partitioning option in the Glue JDBC connection.. Partitioning the table on a key column (e.g., primary key) allows Glue to read in parallel from multiple partitions, reducing the load on the database and improving performance. Option B is wrong because incremental extraction is for ongoing changes, not for an initial full load; it doesn't address the immediate performance issue of extracting 500 GB. Option C is wrong because using a read replica offloads read traffic but does not inherently improve parallelism; partitioning is still needed for performance. Option D is wrong because simply increasing the number of workers may overwhelm the database with more simultaneous connections without partitioning, potentially causing performance degradation.

Answer

Use an incremental extraction strategy with a watermark column to reduce the amount of data read each time.

Answer

Create a read replica of the MySQL database and configure the Glue job to read from the replica.

Answer

Increase the number of Glue workers to 20 to increase parallelism.

Question 19

A gaming company collects player event data from mobile devices. The data is sent to an Amazon API Gateway endpoint, which triggers an AWS Lambda function that writes the data to an Amazon DynamoDB table. The company wants to also store the data in Amazon S3 for historical analysis. The data volume is about 100 GB per day. The data engineer needs to design a solution to copy data from DynamoDB to S3 with minimal impact on the DynamoDB table. What should the data engineer do?

Accepted Answer

Enable DynamoDB Streams on the table and configure a Lambda function to write changes to S3.. Using DynamoDB Streams with a Lambda function that writes to S3 enables real-time replication with minimal impact on the DynamoDB table. Option B is wrong because creating a global secondary index does not export data to S3; it is used to improve query performance. Option C is wrong because using AWS Glue to scan the DynamoDB table would consume read capacity units, potentially impacting performance. Option D is wrong because the DynamoDB Export to S3 feature is a one-time or scheduled export, not continuous, and may not be suitable for near-real-time needs.

Answer

Create a global secondary index on the table and export the index to S3.

Answer

Use AWS Glue to scan the DynamoDB table and write results to S3 every hour.

Answer

Use the DynamoDB Export to S3 feature to export the entire table daily.

Question 20

A company runs a data pipeline that ingests streaming data via Amazon Kinesis Data Streams, processes it with an AWS Lambda function, and stores results in Amazon DynamoDB. The Lambda function sometimes fails due to 'ProvisionedThroughputExceededException' on the DynamoDB table. Which combination of steps should a data engineer take to resolve this issue?

Accepted Answer

Enable DynamoDB auto scaling and configure a dead-letter queue for the Lambda function.. Enabling DynamoDB auto scaling allows the table to adjust its provisioned throughput based on actual traffic patterns, which helps prevent 'ProvisionedThroughputExceededException' when the Lambda function writes to DynamoDB. Additionally, configuring a dead-letter queue (DLQ) for the Lambda function ensures that records that fail due to throttling are captured and can be reprocessed later, preventing data loss. Option B is incorrect because increasing the Lambda function timeout does not address DynamoDB throughput limits. Option C is incorrect because increasing the number of Kinesis shards may increase the rate of Lambda invocations, potentially worsening the throttling issue. Option D is incorrect because increasing Lambda reserved concurrency could allow more concurrent invocations, which may exacerbate throughput exceedance, and disabling retries would cause data loss.

Answer

Increase the Lambda function timeout and enable batch windows.

Answer

Increase the number of Kinesis shards to reduce Lambda invocations.

Answer

Increase Lambda reserved concurrency and disable retries.