Question 1

A Data Engineering Team is working on a project where they need to run SQL queries on their Amazon Redshift cluster from various applications and microservices.

These applications are diverse, running on different platforms and languages, and the team requires a simple, scalable, and secure way to execute these queries without managing database connections. They also need the ability to retrieve query results asynchronously.

Which Amazon Redshift feature should the team use to meet these requirements most effectively?

Options :

A : Establish direct JDBC/ODBC connections from each application to the Redshift cluster for query execution.

B : Implement AWS Lambda functions to manage Redshift query executions and handle database connections.

C : Use Amazon Redshift Spectrum for querying data across applications without direct cluster access.

D : Utilize the Amazon Redshift Data API to execute queries and retrieve results asynchronously without managing database connections.

Answer: D

Question 2

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?

Options :

A :

B :

C :

D :

Answer: A

Question 3

The Data Engineering Team at an online retail company is using Amazon SageMaker Data Wrangler to clean and transform sales data before loading it into their analytics platform. The dataset includes transaction dates, product categories, sales amounts, and customer demographics. The team noted inconsistencies in date formats and several categorical fields with unstandardized text entries.

Which steps should they take in Data Wrangler to resolve these issues effectively?

Options :

A : In Data Wrangler, first perform statistical analysis on all numerical fields to identify outliers, then address the date format inconsistencies and unstandardized categorical fields using a combination of built-in string functions and manual adjustments.

B : Use Data Wrangler to transform date fields into a uniform format by selecting the appropriate date functions and employ the built-in string functions to standardize and clean categorical fields, then proceed to export the data for further processing.

C : Apply transformations in Data Wrangler to convert date fields into a uniform format using its built-in date functions. For the categorical fields, leverage the 'Find and Replace' tool to standardize text entries before exporting the dataset.

D : Utilize Data Wrangler to standardize date formats using custom Python scripts. For categorical fields, manually create new columns to capture standardized text entries, then export the data for analysis.

Answer: B

Question 4

A financial services company is using Amazon RDS for PostgreSQL to manage sensitive customer data. The database is accessed by various applications deployed in an AWS environment. The company's security policy mandates that the data must be encrypted at rest. The database administrator needs to enable encryption for the existing unencrypted database with minimal downtime.

Which of the following approaches should the administrator take?

Options :

A : Enable encryption on the existing RDS instance directly via the AWS Management Console.

B : Take a snapshot of the existing RDS instance, copy the snapshot with encryption enabled, and then restore the database from the encrypted snapshot.

C : Create a new RDS instance with encryption enabled and migrate the data from the existing unencrypted database using AWS Database Migration Service (DMS).

D : Use AWS Key Management Service (KMS) to encrypt the existing database file at the storage level.

Answer: B

Question 5

A company stores operational data in Amazon S3 using the S3 Standard storage class. Data usage analysis shows that most files are frequently accessed for the first 6 months, occasionally accessed for the next 18 months, and rarely accessed thereafter. The company seeks a cost-effective storage strategy that maintains high availability throughout the data lifecycle.

Which S3 Lifecycle policy should the company implement for their data storage needs?

Options :

A : Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months, then to S3 Glacier after 2 years.

B : Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months, then to S3 Glacier Deep Archive after 2 years.

C : Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months, then to S3 Glacier Deep Archive after 2 years.

D : Transition objects to S3 Intelligent-Tiering after 6 months, then to S3 Glacier Deep Archive after 2 years.

Answer: C

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