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Data-Engineer-Associate Practice Questions

AWS Certified Data Engineer - Associate (DEA-C01)

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Total Questions : 289

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Question # 61

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.)

Options:

Turn on the public access setting for the DB instance.

Update the security group of the DB instance to allow only Lambda function invocations on the database port.

Configure the Lambda function to run in the same subnet that the DB instance uses.

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.

Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

Discussion 0

Answer:

C, 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:

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.

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.

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

Network ACLs

Question # 62

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.

The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.

Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.

Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.

Use S3 Intelligent-Tiering. Use the default access tier.

Discussion 0

Question # 63

A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.

Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.

Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.

Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

Discussion 0

Question # 64

A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.

The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.

Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)

Options:

Create views of the tables that need to be shared. Include only the required columns.

Create an Amazon Redshift data than that includes the tables that need to be shared.

Create an Amazon Redshift managed VPC endpoint in the marketing team ' s account. Grant the marketing team access to the views.

Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.

Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team ' s account.

Discussion 0

Question # 65

A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.

Options:

Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.

Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.

Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.

Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.

Discussion 0

Question # 66

A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.

The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to fetch both datasets. Use AWS Lambda functions to correlate the datasets. Use AWS Step Functions to orchestrate the process.

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with the sales opportunities. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the process.

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use AWS Glue to fetch sales records from the MySQL database. Correlate the sales records with sales opportunities. Use AWS Step Functions to orchestrate the process.

Use Amazon AppFlow to fetch sales opportunities from Salesforce. Use Amazon Kinesis Data Streams to fetch sales records from the MySQL database. Use Amazon Managed Service for Apache Flink to correlate the datasets. Use AWS Step Functions to orchestrate the process.

Discussion 0

Question # 67

A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.

Which solution will meet these requirements MOST cost-effectively?

Options:

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.

Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.

Discussion 0

Question # 68

A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.

Recently, the vendor asked the company to transfer the vendor ' s data into the vendor ' s Amazon S3 bucket once each week.

Which solution will meet this requirement?

Options:

Create an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor ' s S3 bucket on a schedule.

Create an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor ' s S3 bucket on a schedule.

Use the Amazon Redshift data sharing feature. Set the vendor ' s S3 bucket as the destination. Configure the source to be as a custom SQL query that selects the required data.

Configure Amazon Redshift Spectrum to use the vendor ' s S3 bucket as destination. Enable data querying in both directions.

Discussion 0

Question # 69

A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.

Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.

Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

Discussion 0

Question # 70

A data engineer needs to query data from multiple sources to generate an annual report. The analytics team uses Amazon Redshift for analysis. The data engineer needs to integrate Amazon Redshift data with 10 years of historical data from Amazon RDS for PostgreSQL and RDS for MySQL. All the databases are in the same VP

The data engineer needs a solution that provides seamless data integration with Amazon Redshift.

Which solution will meet these requirements in the MOST cost-effective way?

Options:

Use federated queries in Amazon Redshift to fetch data from RDS for PostgreSQL and RDS for MySQL. Apply the necessary transformations within Amazon Redshift.

Use the SELECT INTO OUTFILE S3 statement to export data from Amazon RDS to Amazon S3. Use the COPY command to load the data into Amazon Redshift.

Create a visual extract, transform, and load (ETL) job in AWS Glue to extract the required data and load it to Amazon Redshift.

Use AWS Database Migration Service (AWS DMS) to ingest data from RDS for PostgreSQL and RDS for MySQL. Implement the necessary transformations within Amazon Redshift.

Discussion 0

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