DATA-ENGINEER-ASSOCIATE CERTIFICATION COST | DATA-ENGINEER-ASSOCIATE VALID TEST LABS

Data-Engineer-Associate Certification Cost | Data-Engineer-Associate Valid Test Labs

Data-Engineer-Associate Certification Cost | Data-Engineer-Associate Valid Test Labs

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q97-Q102):

NEW QUESTION # 97
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.)

  • A. Use 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.
  • B. Verify 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.
  • C. Check 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.
  • D. Query 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.
  • E. Check 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).

Answer: B,D

Explanation:
To identify the reason why the Step Functions state machine is not able to run the EMR jobs, the company should take the following steps:
Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. The state machine code should have an IAM role that allows it to invoke the EMR APIs, such as RunJobFlow, AddJobFlowSteps, and DescribeStep. The state machine code should also have IAM permissions to access the Amazon S3 buckets that the EMR jobs use as input and output locations. The company can use Access Analyzer for S3 to check the access policies and permissions of the S3 buckets12. Therefore, option B is correct.
Query the flow logs for the VPC. The flow logs can provide information about the network traffic to and from the EMR cluster that is launched in the VPC. The company can use the flow logs to determine whether the traffic that originates from the EMR cluster can successfully reach the data providers, such as Amazon RDS, Amazon Redshift, or other external sources. The company can also determine whether any security group that might be attached to the EMR cluster allows connections to the data source servers on the informed ports. The company can use Amazon VPC Flow Logs or Amazon CloudWatch Logs Insights to query the flow logs3 . Therefore, option D is correct.
Option A is incorrect because it suggests using AWS CloudFormation to automate the Step Functions state machine deployment. While this is a good practice to ensure consistency and repeatability of the deployment, it does not help to identify the reason why the state machine is not able to run the EMR jobs. Moreover, creating a step to pause the state machine during the EMR jobs that fail and wait for a human user to send approval through an email message is not a reliable way to troubleshoot the issue. The company should use the Step Functions console or API to monitor the execution history and status of the state machine, and use Amazon CloudWatch to view the logs and metrics of the EMR jobs .
Option C is incorrect because it suggests changing the AWS Step Functions state machine code to use Amazon EMR on EKS. Amazon EMR on EKS is a service that allows you to run EMR jobs on Amazon Elastic Kubernetes Service (Amazon EKS) clusters. While this service has some benefits, such as lower cost and faster execution time, it does not support all the features and integrations that EMR on EC2 does, such as EMR Notebooks, EMR Studio, and EMRFS. Therefore, changing the state machine code to use EMR on EKS may not be compatible with the existing data pipeline and may introduce new issues.
Option E is incorrect because it suggests checking the retry scenarios that the company configured for the EMR jobs. While this is a good practice to handle transient failures and errors, it does not help to identify the root cause of why the state machine is not able to run the EMR jobs. Moreover, increasing the number of seconds in the interval between each EMR task may not improve the success rate of the jobs, and may increase the execution time and cost of the state machine. Configuring an Amazon SNS topic to store the error messages may help to notify the company of any failures, but it does not provide enough information to troubleshoot the issue.
Reference:
1: Manage an Amazon EMR Job - AWS Step Functions
2: Access Analyzer for S3 - Amazon Simple Storage Service
3: Working with Amazon EMR and VPC Flow Logs - Amazon EMR
[4]: Analyzing VPC Flow Logs with Amazon CloudWatch Logs Insights - Amazon Virtual Private Cloud
[5]: Monitor AWS Step Functions - AWS Step Functions
[6]: Monitor Amazon EMR clusters - Amazon EMR
[7]: Amazon EMR on Amazon EKS - Amazon EMR


NEW QUESTION # 98
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

  • A. Use the SUPER data type to store the data in the Amazon Redshift table.
  • B. Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.
  • C. Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.
  • D. Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.

Answer: A

Explanation:
In Amazon Redshift, the SUPER data type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. The SUPER data type also works seamlessly with Redshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company's need to use combined datasets to identify potential new customers.
Using the SUPER data type also allows for automatic parsing and query processing of nested data structures through Amazon Redshift's PARTITION BY and JSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.
Reference:
Amazon Redshift Documentation - SUPER Data Type
AWS Certified Data Engineer - Associate Training: Building Batch Data Analytics Solutions on AWS AWS Certified Data Engineer - Associate Study Guide By directly leveraging the capabilities of Redshift with the SUPER data type, the data engineer ensures streamlined JSON ingestion with minimal effort while maintaining query efficiency.


NEW QUESTION # 99
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?

  • A. AWS Glue Data Catalog
  • B. Amazon Managed Streaming for Apache Kafka (Amazon MSK)
  • C. Amazon Kinesis
  • D. Amazon AppFlow

Answer: D

Explanation:
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and AmazonRedshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks.
Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines.
References:
Amazon AppFlow
Amazon AppFlow | SaaS Integrations List
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions


NEW QUESTION # 100
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

  • A. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
  • B. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
  • C. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
  • D. Specify a combination of distribution, sort, and partition keys for all tables.

Answer: D

Explanation:
This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables. However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. Reference:
Choose the best distribution style
Distribution styles
Working with data distribution styles


NEW QUESTION # 101
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?

  • A. Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.
  • B. Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2.
  • C. Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.
  • D. Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.

Answer: B

Explanation:
The AWS Transfer Family server's security policy can be updated to enforce TLS 1.2 or higher, ensuring compliance with company policy for encrypted data transfers.
AWS Transfer Family Security Policy:
AWS Transfer Family supports setting a minimum TLS version through its security policy configuration. This ensures that only connections using TLS 1.2 or above are allowed.
Reference:
Alternatives Considered:
A (Generate new SSH keys): SSH keys are unrelated to TLS and do not enforce encryption protocols like TLS 1.2.
B (Update security group rules): Security groups control IP-level access, not TLS versions.
D (Install SSL certificate): SSL certificates ensure secure connections, but the TLS version is controlled via the security policy.
AWS Transfer Family Documentation


NEW QUESTION # 102
......

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