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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q58-Q63):

NEW QUESTION # 58
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

A. Configure the Lambda function to run in the same subnet that the DB instance uses.
B. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.
C. Turn on the public access setting for the DB instance.
D. Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.
E. Update the security group of the DB instance to allow only Lambda function invocations on the database port.

Answer: A,D

Explanation:
To enable the Lambda function to connect to the RDS DB instance privately without using the public internet, the best combination of steps is to configure the Lambda function to run in the same subnet that the DB instance uses, and attach the same security group to the Lambda function and the DB instance. This way, the Lambda function and the DB instance can communicate within the same private network, and the security group can allow traffic between them on the database port. This solution has the least operational overhead, as it does not require any changes to the public access setting, the network ACL, or the security group of the DB instance.
The other options are not optimal for the following reasons:
* A. Turn on the public access setting for the DB instance. This option is not recommended, as it would expose the DB instance to the public internet, which can compromise the security and privacy of the data. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
* B. Update the security group of the DB instance to allow only Lambda function invocations on the database port. This option is not sufficient, as it would only modify the inbound rules of the security group of the DB instance, but not the outbound rules of the security group of the Lambda function.
Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
* E. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port. This option is not necessary, as the network ACL of the private subnet already allows all traffic within the subnet by default. Moreover, this option would not enable the Lambda function to connect to the DB instance privately, as it would still require the Lambda function to use the public internet to access the DB instance.
:
1: Connecting to an Amazon RDS DB instance
2: Configuring a Lambda function to access resources in a VPC
3: Working with security groups
4: Network ACLs

NEW QUESTION # 59
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?

A. Run the VACUUM REINDEX command against the identified tables.
B. Run the ANALYZE command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
C. Run the VACUUM RECLUSTER command against the identified tables.
D. Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.

Answer: D

Explanation:
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.
The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
Option A (ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.
Options C and D (VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
Reference:
Amazon Redshift ANALYZE COMPRESSION Command

NEW QUESTION # 60
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?

A. Launch 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.
B. Launch 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.
C. Launch 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.
D. Launch 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.

Answer: B

Explanation:
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost.
Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data.
The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D)would not work, as the data on the instance store volume would be lost if the instance is terminated. References:
Amazon EC2 Instance Store
Amazon EBS Volumes
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2

NEW QUESTION # 61
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable information (PII) about customers within the dat a. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

A. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
B. Implement custom data quality rules in Data Brew. Apply the custom rules across datasets.
C. Manually review the data for custom PII categories.
D. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.

Answer: B

Explanation:
The data engineer needs to detect custom categories of PII within the data lake using AWS Glue DataBrew. While DataBrew provides standard data quality rules, the solution must support custom PII categories.
Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
This option is the most efficient because DataBrew allows the creation of custom data quality rules that can be applied to detect specific data patterns, including custom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.
Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.
Reference:
AWS Glue DataBrew Documentation

NEW QUESTION # 62
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. AWS Lambda
C. Amazon EMR
D. AWS Glue

Answer: C

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 # 63
......

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