DEA-C01 Question Bank · Latest 50 Questions Free Preview (Page 5/5)
Questions 41–50 of the latest 50 DEA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 306 questions online.
Question 41 · No. 297 · Single choice
A data engineer needs to deploy a complex pipeline. The stages of the pipeline must be able to run a script. The data engineer must use only fully managed and serverless services in the pipeline. Which solution will meet these requirements?
- A. Deploy AWS Glue jobs and workflows. Use AWS Glue to run the jobs and workflows on a schedule.
- B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to build and schedule the pipeline.
- C. Deploy the script to Amazon EC2 instances. Use Amazon EventBridge to run the script on a schedule.
- D. Use Aws Glue DataBrew to build the pipeline. Use Amazon EventBridge to run the pipeline on a schedule.
Answer: A
Question 42 · No. 298 · Single choice
A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted. The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains. Which solution will meet these requirements?
- A. Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.
- B. Use AWS Glue Data Catalog views to perform analysis. Use AWS CloudTrail logs to audit data access. Use AWS Lake Formation to manage access permissions. Use security definer views to mask PII.
- C. Use AWS Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.
- D. Create materialized views and enable Amazon Redshift datashares for each domain. Configure cross-domain access policies.
Answer: A
Explanation: Option A is the most comprehensive solution that addresses all the requirements. It uses Athena views for on-demand analysis across domains and enables those views to be queried from Amazon Redshift for complex cross-domain analytics. AWS CloudTrail provides auditing capabilities required by the compliance team, and AWS Lake Formation provides fine-grained access control to restrict access to PII data. This solution leverages native AWS services to create a scalable data mesh architecture that supports both Athena (on-demand analysis) and Redshift (complex reports) use cases while maintaining proper access controls and audit capabilities.
Question 43 · No. 299 · Single choice
A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team. Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an Amazon Simple Notification Service (Amazon SNS) topic with an AWS Lambda function subscriber that evaluates the error type and forwards the error to the appropriate email addresses.
- B. Configure Amazon EventBridge rules with distinct event patterns for each error type. Route each error type to a dedicated Amazon Simple Notification Service (Amazon SNS) topic for team-specific alerts.
- C. Use Amazon Simple Queue Service (Amazon SQS) with message attributes to categorize errors. Allow each team to poll their respective SQS queue for relevant errors.
- D. Set up Amazon CloudWatch alarms with different metrics for each error type. Invoke a different Amazon Simple Notification Service (Amazon SNS) notification each time a metrics threshold is crossed.
Answer: B
Explanation: Option B uses Amazon EventBridge with distinct event patterns for each error type, routing them to dedicated SNS topics for team-specific alerts. EventBridge natively supports event filtering and routing in real-time without requiring polling (unlike SQS in option C) or additional compute layers (unlike Lambda in option A). It's a fully managed, serverless solution with minimal operational overhead. CloudWatch alarms (option D) are designed for metric threshold violations, not for real-time transaction error processing, and would require more configuration and management. Option C with SQS polling would introduce delays, contradicting the 'without any delay' requirement. EventBridge directly integrates with the data pipeline events and SNS provides instant notifications to each team via their dedicated topic.
Question 44 · No. 300 · Single choice
A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second. Which solution will meet these requirements?
- A. Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.
- B. Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.
- C. Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift Java Database Connectivity (JDBC) connector to establish a database connection. Use the database connection to perform all read and write operations.
- D. Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.
Answer: D
Explanation: Option D (Amazon DynamoDB with provisioned capacity) is the correct choice for the following reasons: 1) API-based access requirement - DynamoDB provides native API-based access through DynamoDB APIs, which directly satisfies the requirement for dependable API-based access to specific records; 2) High concurrency requirement - DynamoDB is specifically designed to handle massive concurrent operations, easily supporting more than 4,000 concurrent writes and 6,500 reads per second; 3) Specific record access - DynamoDB uses primary keys to provide direct, fast access to specific records, which is ideal for the log table scenario; 4) Continuous ingestion - DynamoDB handles continuous ingestion well with its scalable architecture. In contrast, Amazon Redshift (Options A and C) is a data warehouse optimized for analytical queries, not high-concurrency OLTP workloads, and has limitations on concurrent connections. Option B with S3 and Redshift Spectrum is designed for analytical queries over large datasets, not for high-frequency transactional operations.
Question 45 · No. 301 · Single choice
A company uses AWS Glue ETL pipelines to process data. The company uses Amazon Athena to analyze data in an Amazon S3 bucket. To better understand shipping timelines, the company decides to collect and store shipping and delivery dates in addition to order data. The company adds a data quality check to ensure that shipping date is greater than order date and that delivery date is greater than shipping date. Orders that fail the quality check must be stored in a second S3 bucket. Which solution will meet these requirements MOST cost-effectively?
- A. Use the AWS Glue DataBrew DATEDIFF function to create two additional columns. Check the new columns.
- B. Use Athena to query all three date columns, and compare the columns.
- C. Use AWS Glue Data Quality to create a custom rule that uses the three date columns.
- D. Use an AWS Glue crawler to populate an AWS Glue Data Catalog. Use the three date columns to create a filter.
Answer: C
Explanation: AWS Glue Data Quality is the purpose-built solution for creating data quality rules within AWS Glue ETL pipelines. It allows creating custom rules that span multiple columns (in this case, comparing order_date, shipping_date, and delivery_date) and can separate failed records into a different destination (the second S3 bucket). Option A uses DataBrew which is a separate visual data preparation service with higher costs. Option B uses Athena which is for querying/analysis, not for ETL data quality validation. Option D uses a crawler which is for schema discovery, not for quality checks. Glue Data Quality is integrated into Glue jobs, making it the most cost-effective choice for this scenario.
Question 46 · No. 302 · Single choice
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket. Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
- B. Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
- C. Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.
- D. Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.
Answer: B
Explanation: AWS Glue DataBrew is a fully managed, serverless, no-code visual data preparation service specifically designed for data quality and data transformation tasks. It allows the data engineer to define data quality rulesets through a visual interface without writing custom code. The profile job can be triggered automatically via Amazon EventBridge when new data arrives in the S3 bucket, eliminating manual intervention. Compared to option A and D (which require writing and maintaining Lambda functions), and option C (which requires provisioning and managing an EMR cluster), Glue DataBrew offers the least operational overhead because it is fully managed and purpose-built for data quality checks.
Question 47 · No. 303 · Single choice
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames. The job is failing with the following error: No space left on device. Which solution will resolve the error?
- A. Use the AWS Glue Spark shuffle manager.
- B. Deploy are Amazon Elastic Block Store (Amazon EBS) volume for the job to use.
- C. Convert the sort-merge join in the job to be a broadcast join.
- D. Convert the DataFrames to DynamicFrames, and perform a DynamicFrame join in the job.
Answer: A
Explanation: The AWS Glue Spark Shuffle Manager plugin is designed to address the 'No space left on device' error during large shuffle operations like sort-merge joins. By default, Spark writes shuffle data to local disk on the worker nodes, which can fill up when handling large datasets. The AWS Glue Spark Shuffle Manager offloads this shuffle data to Amazon S3, eliminating the local disk space constraint. Option B is not feasible because AWS Glue is a serverless service that does not support attaching custom EBS volumes. Option C is inappropriate because broadcast joins are suitable for joining a large DataFrame with a small one, not two equally large DataFrames (which would cause memory exhaustion). Option D does not solve the underlying disk space issue during shuffle operations.
Question 48 · No. 304 · Multiple choice
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 ТВ of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework. What combination of steps will meet these requirements? (Choose two.)
- A. Create a key named --conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.
- B. Specify the path to a specific version of Iceberg by using the -extra-jars job parameter. Set Iceberg as a value for the datalake-formats job parameter.
- C. Set Iceberg as a value for the --datalake-formats job parameter.
- D. Set the --enable-auto-scaling parameter to true.
- E. Add the --job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.
Answer: C, D
Question 49 · No. 305 · Single choice
A data engineer at a company is optimizing extract, transform, and load (ETL) workflows. The current architecture uses Amazon EMR and Apache Spark for large-scale transformations and AWS Glue for other ETL tasks. The workflows load processed data into an Amazon S3 based data lake. The company wants to move to a fully managed serverless solution that can orchestrate multiple ETL jobs and automate execution. The new solution must continue to use Spark to process data. The company needs to orchestrate and automate the ETL workflows with minimal manual intervention. Which solution will meet these requirements?
- A. Migrate all ETL jobs to AWS Glue. Use AWS Glue workflows to orchestrate the pipeline.
- B. Configure AWS Step Functions and Amazon EventBridge to orchestrate and invoke ETL workflows in AWS Glue and Amazon EMR.
- C. Configure AWS Lambda functions to process Amazon S3 event notifications for data transformation tasks when new data is uploaded.
- D. Use Amazon Managed Workflows for Apache Airflow automatic scheduling to orchestrate the Spark-based ETL jobs.
Answer: A
Question 50 · No. 306 · Single choice
A company generates yearly financial statements for customers and stores the statements in an Amazon S3 bucket. Customers rarely access the documents after 1 week. The company must retain the statements for 7 years. The statements must remain readily accessible for customers. Which solution will meet these requirements in the MOST cost-effective way?
- A. Create an S3 Lifecycle rule to transition objects to S3 Glacier Deep Archive after 7 days. Expire the objects after 7 years.
- B. Set the S3 bucket to use S3 Intelligent-Tiering when new objects are uploaded. Set objects to expire after 7 years.
- C. Create an S3 Lifecycle rule to transition objects to S3 Glacier Instant Retrieval after 7 days. Expire the objects after 7 years.
- D. Set the S3 bucket to use S3 Glacier Instant Retrieval when new objects are uploaded. Create an AWS Lambda function that runs daily to delete any objects that are older than 7 years.
Answer: C
Explanation: Option C is the most cost-effective solution that meets all requirements. S3 Glacier Instant Retrieval provides millisecond retrieval times, which satisfies the 'readily accessible' requirement, while being significantly cheaper than S3 Standard for long-term storage. The 7-day transition to Glacier Instant Retrieval (after the frequent access period ends) followed by expiration after 7 years optimally matches the access pattern described. Option A (Deep Archive) fails because retrieval takes 12-48 hours, not 'readily accessible.' Option B (Intelligent-Tiering) incurs per-object monitoring fees that make it more expensive for predictable access patterns. Option D is suboptimal because it places new objects in the more expensive Glacier Instant Retrieval immediately, rather than after the initial high-access period in cheaper S3 Standard.