DEA-C01 practice questions 141–150

1. Question 141 · 313 in bank

   A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3. Which solution will meet these requirements in the MOST operationally efficient way?

   • ACreate a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
   • BSchedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
   • CUse a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
   • DCreate an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

   Show answer

   Answer: A

   Explanation

   Option A involves creating a view in the EC2 instance-based SQL Server databases that contains the required data elements. An AWS Glue job is then created to select the data directly from the view and transfer the data in Parquet format to an S3 bucket. This job is scheduled to run every day. This approach is operationally efficient as it leverages managed services (AWS Glue) and does not require additional transformation steps. Option D involves creating an AWS Lambda function that queries the EC2 instance-based databases using JDBC. The Lambda function is configured to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. This approach could work, but managing and scheduling Lambda functions could add operational overhead compared to using managed services like AWS Glue.

   Source: Adapted from community discussion — bakarys

   References

   • AWS documentation
   • AWS documentation

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2. Question 142 · 313 in bank

   Select 2

   A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations. Which combination of AWS services will implement a data mesh? (Choose two.)

   • AUse Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
   • BUse Amazon S3 for data storage. Use Amazon Athena for data analysis.
   • CUse AWS Glue DataBrew for centralized data governance and access control.
   • DUse Amazon RDS for data storage. Use Amazon EMR for data analysis.
   • EUse AWS Lake Formation for centralized data governance and access control.

   Show answer

   Answer: BE

   Explanation

   The answer is B and E. The data mesh implementation uses Amazon S3 and Athena for data storage and analysis, and AWS Lake Formation for centralized data governance and access control. When combined with AWS Glue, you can efficiently manage your data.

   Source: Adapted from community discussion — hsnin

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3. Question 143 · 313 in bank

   A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues. Which table views should the data engineer use to meet this requirement?

   • ASTL_USAGE_CONTROL
   • BSTL_ALERT_EVENT_LOG
   • CSTL_QUERY_METRICS
   • DSTL_PLAN_INFO

   Show answer

   Answer: B

   Explanation

   B STL_ALERT_EVENT_LOG records any alerts/notifications related to queries or user-defined performance thresholds. This would capture optimizer alerts about potential performance issues. STL_PLAN_INFO provides detailed info on execution plans. The optimizer statistics and warnings provide insight into problematic query plans. STL_USAGE_CONTROL limits user activity but does not log anomalies. STL_QUERY_METRICS has execution stats but no plan diagnostics. By enabling alerts and checking STL_ALERT_EVENT_LOG and STL_PLAN_INFO, the data engineer can best detect and troubleshoot queries flagged by the optimizer as problematic before they impair performance. This meets the requirement to catch potential long running queries.

   Source: Adapted from community discussion — Felix_G

   References

   • AWS documentation
   • AWS documentation

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4. Question 144 · 313 in bank

   A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file. Which solution will meet these requirements MOST cost-effectively?

   • AUse an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
   • BCreate an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
   • CUse an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
   • DCreate an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

   Show answer

   Answer: D

   Explanation

   Athena is optimized for querying data stored in Parquet format. It can efficiently scan only the necessary columns for a specific query, reducing the amount of data processed. This translates to faster query execution times and lower query costs for data analysts who primarily focus on one or two columns

   Source: Adapted from community discussion — pypelyncar

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5. Question 145 · 313 in bank

   Select 2

   A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage. A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region. Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

   • AUse data filters for each Region to register the S3 paths as data locations.
   • BRegister the S3 path as an AWS Lake Formation location.
   • CModify the IAM roles of the HR departments to add a data filter for each department's Region.
   • DEnable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.
   • ECreate a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.

   Show answer

   Answer: BD

   Explanation

   Registering the S3 path as an AWS Lake Formation location is the first step in leveraging Lake Formation's data governance and access control capabilities. This allows the data engineering team to centrally manage and govern the data stored in the S3 data lake. Enabling fine-grained access control in AWS Lake Formation and adding a data filter for each Region is the key step to achieve the desired access control. Data filters in Lake Formation allow you to define row-level and column-level access policies based on specific conditions or attributes, such as the Region in this case

   Source: Adapted from community discussion — pypelyncar

   References

   • AWS documentation
   • AWS documentation

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6. Question 146 · 313 in bank

   Select 2

   A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift. The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs. Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)

   • AUse AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.
   • BVerify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.
   • CCheck for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).
   • DQuery the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.
   • ECheck the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.

   Show answer

   Answer: BD

   Explanation

   Permissions of course and we need to see if the traffic is blocked at any hops because they mention that EMR is IN vpc so... flow-logs

   Source: Adapted from community discussion — GiorgioGss

   References

   • AWS documentation
   • AWS documentation

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7. Question 147 · 313 in bank

   A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated. A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data. Which solution will meet this requirement?

   • ALaunch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.
   • BLaunch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.
   • CLaunch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.
   • DLaunch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.

   Show answer

   Answer: C

   Explanation

   CCCCCCC - you need to attach an extra EBS volume When an instance terminates, the value of the DeleteOnTermination attribute for each attached EBS volume determines whether to preserve or delete the volume. By default, the DeleteOnTermination attribute is set to True for the root volume. ref: https://repost.aws/knowledge-center/deleteontermination-ebs

   Source: Adapted from community discussion — khchan123

   References

   • repost.aws documentation
   • AWS documentation

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8. Question 148 · 313 in bank

   A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics. Which solution will give the company the ability to use Spark to access Athena?

   • AAthena query settings
   • BAthena workgroup
   • CAthena data source
   • DAthena query editor

   Show answer

   Answer: B

   Explanation

   https://docs.aws.amazon.com/athena/latest/ug/notebooks-spark-getting-started.html To get started with Apache Spark on Amazon Athena, you must first create a Spark enabled workgroup. After you switch to the workgroup, you can create a notebook or open an existing notebook. When you open a notebook in Athena, a new session is started for it automatically and you can work with it directly in the Athena notebook editor.

   Source: Adapted from community discussion — andrologin

   References

   • AWS documentation
   • AWS documentation

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9. Question 149 · 313 in bank

   A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01. A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket. Which solution will meet these requirements with the LEAST latency?

   • ASchedule an AWS Glue crawler to run every morning.
   • BManually run the AWS Glue CreatePartition API twice each day.
   • CUse code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.
   • DRun the MSCK REPAIR TABLE command from the AWS Glue console.

   Show answer

   Answer: C

   Explanation

   Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call. This approach ensures that the Data Catalog is updated as soon as new data is written to S3, providing the least latency in reflecting new partitions.

   Source: Adapted from community discussion — rralucard_

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10. Question 150 · 313 in bank

    A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data. Which AWS service or feature will meet these requirements with the LEAST operational overhead?

    • AAmazon Managed Streaming for Apache Kafka (Amazon MSK)
    • BAmazon AppFlow
    • CAWS Glue Data Catalog
    • DAmazon Kinesis

    Show answer

    Answer: B

    Explanation

    That's exactly the purpose of AppFlow: "fully-managed integration service that enables you to securely exchange data between software as a service (SaaS) applications, such as Salesforce, and AWS services, such as Amazon Simple Storage Service (Amazon S3) and Amazon Redshift. For example, you can ingest contact records from Salesforce to Amazon Redshift or pull support tickets from Zendesk to an Amazon S3 bucket." https://docs.aws.amazon.com/appflow/latest/userguide/what-is-appflow.html

    Source: Adapted from community discussion — tgv

    References

    • AWS documentation
    • AWS documentation

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DEA-C01 practice, question 141. 313 questions available.