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Pass the Amazon Web Services AWS Certified Data Engineer Data-Engineer-Associate Questions and answers with Dumpstech

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Viewing questions 51-60 out of questions

Questions # 51:

A company wants to combine data from multiple software as a service (SaaS) applications for analysis.

A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.

Which solution will meet these requirements in the MOST operationally efficient way?

Options:

Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight

Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.

Use Amazon AppFlow to create a Row for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.

Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.

Questions # 52:

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.

The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.

Which rule will meet these requirements?

Options:

DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Questions # 53:

A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.

The company wants to minimize the effort and time required to incorporate third-party datasets.

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

Options:

Use API calls to access and integrate third-party datasets from AWS Data Exchange.

Use API calls to access and integrate third-party datasets from AWS

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Answer

Explanation

AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.

The other options are not optimal for the following reasons:

B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.

C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .

D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .

[:, 1: AWS Data Exchange User Guide, 2: AWS Data Exchange FAQs, 3: AWS Glue Developer Guide, : AWS CodeCommit User Guide, : Amazon Kinesis Data Streams Developer Guide, : Amazon Elastic Container Registry User Guide, : Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source, , ]

Questions # 54:

A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.

Which solution will meet these requirements?

Options:

Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

Create an JAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Questions # 55:

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

Options:

Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Questions # 56:

A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.

Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

Options:

Set up an AWS DMS replication instance in Account_B in eu-west-1.

Set up an AWS DMS replication instance in Account_B in eu-east-1.

Set up an AWS DMS replication instance in a new AWS account in eu-west-1

Set up an AWS DMS replication instance in Account_A in eu-east-1.

Questions # 57:

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?

Options:

Generate new SSH keys for the Transfer Family server. Make the old keys and the new keys available for use.

Update the security group rules for the on-premises network to allow only connections that use TLS 1.2 or above.

Update the security policy of the Transfer Family server to specify a minimum protocol version of TLS 1.2.

Install an SSL certificate on the Transfer Family server to encrypt data transfers by using TLS 1.2.

Questions # 58:

A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second.

Options:

Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.

Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.

Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift JDBC connector to establish a database connection. Use the database connection to perform all read and write operations.

Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.

Questions # 59:

A company has an Amazon Redshift data warehouse that users access by using a variety of IAM roles. More than 100 users access the data warehouse every day.

The company wants to control user access to the objects based on each user's job role, permissions, and how sensitive the data is.

Which solution will meet these requirements?

Options:

Use the role-based access control (RBAC) feature of Amazon Redshift.

Use the row-level security (RLS) feature of Amazon Redshift.

Use the column-level security (CLS) feature of Amazon Redshift.

Use dynamic data masking policies in Amazon Redshift.

Questions # 60:

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?

Options:

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

Amazon AppFlow

AWS Glue Data Catalog

Amazon Kinesis

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Viewing questions 51-60 out of questions