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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q120-Q125):

NEW QUESTION # 120
A company has a data pipeline that uses an Amazon RDS instance, AWS Glue jobs, and an Amazon S3 bucket. The RDS instance and AWS Glue jobs run in a private subnet of a VPC and in the same security group.
A use' made a change to the security group that prevents the AWS Glue jobs from connecting to the RDS instance. After the change, the security group contains a single rule that allows inbound SSH traffic from a specific IP address.
The company must resolve the connectivity issue.
Which solution will meet this requirement?

A. Add an inbound rule that allows all TCP traffic on all UDP ports. Set the private IP address of the RDS instance as the source.
B. Add an inbound rule that allows all TCP traffic on all TCP ports. Set the DNS name of the RDS instance as the source.
C. Replace the source of the existing SSH rule with the private IP address of the RDS instance. Create an outbound rule with the same source, destination, and protocol as the inbound SSH rule.
D. Add an inbound rule that allows all TCP traffic on all TCP ports. Set the security group as the source.

Answer: D

NEW QUESTION # 121
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
B. Store the credentials in a configuration file that is in an Amazon S3 bucket.
C. Store the credentials in AWS Secrets Manager.
D. Grant the AWS Glue job 1AM role access to the stored credentials.
E. Store the credentials in the AWS Glue job parameters.

Answer: C,D

Explanation:
AWS Secrets Manager is a service that allows you to securely store and manage secrets, such as database credentials, API keys, passwords, etc. You can use Secrets Manager to encrypt, rotate, and audit your secrets, as well as to control access to them using fine-grained policies. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API).
Storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials will meet the requirements, as it will remediate the security vulnerability in the AWS Glue job and securely store the credentials. By using AWS Secrets Manager, you can avoid hard coding the credentials in the job script, which is a bad practice that exposes the credentials to unauthorized access or leakage. Instead, you can store the credentials as a secret in Secrets Manager and reference the secret name or ARN in the job script. You can also use Secrets Manager to encrypt thecredentials using AWS Key Management Service (AWS KMS), rotate the credentials automatically or on demand, and monitor the access to the credentials using AWS CloudTrail. By granting the AWS Glue job 1AM role access to the stored credentials, you can use the principle of least privilege to ensure that only the AWS Glue job can retrieve the credentials from Secrets Manager. You can also use resource-based or tag-based policies to further restrict the access to the credentials.
The other options are not as secure as storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials. Storing the credentials in the AWS Glue job parameters will not remediate the security vulnerability, as the job parameters are still visible in the AWS Glue console and API. Storing the credentials in a configuration file that is in an Amazon S3 bucket and accessing the credentials from the configuration file by using the AWS Glue job will not be as secure as using Secrets Manager, as the configuration file may not be encrypted or rotated, and the access to the file may not be audited or controlled. References:
AWS Secrets Manager
AWS Glue
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

NEW QUESTION # 122
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A. Option B
B. Option A
C. Option C
D. Option D

Answer: B

Explanation:
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa'; This statement inserts only the cities and states where the country column has a value of 'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
Reference:
Amazon Athena SQL Reference
Creating Tables in Athena

NEW QUESTION # 123
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

A. Amazon Redshift
B. Amazon EMR
C. AWS Lambda
D. AWS Glue

Answer: B

Explanation:
AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. References:
* AWS Glue
* AWS Glue Data Catalog
* AWS Glue Workflows
* [AWS Glue Job Bookmarking]
* [AWS Glue Crawlers]
* [AWS Glue Schema Registry]
* [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

NEW QUESTION # 124
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Select TWO.)

A. Use AWS Step Functions to orchestrate the process of updating the Data Catalog based on 53 events that the SQS queue receives.
B. Use an AWS Lambda function to directly update the Data Catalog based on S3 events that the SQS queue receives.
C. Create an S3 event-based AWS Glue crawler to consume events from the SQS queue.
D. Manually initiate the AWS Glue crawler to perform updates to the Data Catalog when there is a change in the S3 bucket.
E. Define a time-based schedule to run the AWS Glue crawler, and perform incremental updates to the Data Catalog.

Answer: B,C

Explanation:
The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.
A . Create an S3 event-based AWS Glue crawler:
An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.
Reference:
C . Use an AWS Lambda function to directly update the Data Catalog:
Lambda can be triggered by S3 events delivered to the SQS queue and can directly update the Glue Data Catalog, ensuring that new data is reflected in near real-time without running a full crawler.
Alternatives Considered:
B (Time-based schedule): Scheduling a crawler to run periodically adds unnecessary latency and operational overhead.
D (Manual crawler initiation): Manually starting the crawler defeats the purpose of automation.
E (AWS Step Functions): Step Functions add complexity that is not needed when Lambda can handle the updates directly.
AWS Glue Event-Driven Crawlers
Using AWS Lambda to Update Glue Catalog

NEW QUESTION # 125
......

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