Question 1

A company specializing in market analysis is looking to enhance its data analytics capabilities by incorporating diverse external datasets, such as financial market data, healthcare statistics, and geographical information. They require an efficient, secure, and straightforward method to access and integrate these varied datasets into their AWS environment for analysis.

Which AWS service should the company use to acquire third-party data sets seamlessly and incorporate them into their analytics workflows?

Options :

A : Use Amazon Kinesis for streaming and analyzing real-time data from various external sources.

B : Deploy AWS Glue to discover and integrate external data sets into their data warehouse.

C : Utilize AWS Data Exchange to find, subscribe to, and use third-party data sets in the AWS cloud.

D : Explore the AWS Data Market for purchasing and downloading various external data sets.

Answer: C

Question 2

A Cloud Data Engineering Team needs to migrate their on-premises Oracle database to Amazon Aurora PostgreSQL. They want to ensure that the schema and the code in the Oracle database, including stored procedures and functions, are compatible with Aurora PostgreSQL. They plan to use AWS tools for this conversion and migration.

What would be the easiest way, using AWS services, to convert the Oracle database schema to be compatible with Aurora PostgreSQL and to perform the subsequent data migration?

Options :

A : Implement AWS Glue ETL for the schema conversion, using its data transformation capabilities, followed by AWS DMS for migrating the data.

B : First, use AWS SCT to convert the Oracle database schema to an Aurora PostgreSQL-compatible format, then employ AWS DMS for the actual data migration.

C : Start with Amazon RDS for Oracle-s built-in schema conversion tool and then use AWS DMS for the data migration to Aurora PostgreSQL.

D : Utilize AWS DMS, including its Schema Conversion feature, for an integrated solution that handles both the conversion of the Oracle database schema and the migration to Amazon Aurora PostgreSQL

Answer: D

Question 3

A company is using AWS Glue to manage their data catalog for various data sources. They have an Amazon S3 bucket that receives a continuous stream of JSON files, which are saved in a nested directory structure based on the ingestion date (e.g., year=2023/month=04/day=05). Each JSON file structure differs slightly from others, causing issues with schema consistency in the Glue Data Catalog.

To ensure accurate schema detection and consistent query results in downstream services like Amazon Athena and Amazon Redshift Spectrum, what should the company do?

Options :

A : Configure an AWS Glue crawler with a custom classifier to run whenever a new file is uploaded to the S3 bucket. The classifier should be designed to handle varying schemas and update the Glue Data Catalog accordingly.

B : Implement an AWS Step Functions workflow that triggers an AWS Glue job to preprocess and homogenize the schema of the new files in a staging area within S3. Use an AWS Glue crawler to update the catalog based on the homogenized data.

C : Use AWS Lambda to trigger an Amazon EMR job for each file uploaded, to standardize the JSON structure and save the transformed data in a new S3 location. Then, crawl this new location with AWS Glue.

D : Enable Amazon S3 Object Lambda to modify the schema of incoming JSON files on-the-fly to conform to a predefined structure before storing them in S3. Set the AWS Glue crawler to run daily to update the catalog.

Answer: A

Question 4

You are a Data Engineer tasked with architecting a solution for an online retail application on AWS. The application must efficiently manage user shopping cart data (stateful transactions) and handle rapid, high-volume event logging for website interactions, like clicks and views (stateless transactions).

Considering the need for high availability, scalability, and near real-time processing, which of the following AWS service combinations is the most appropriate for handling these specific transaction types?

Options :

A : Use Amazon DynamoDB with Global Tables for the shopping cart data to enable stateful transactions across multiple regions, and AWS Lambda combined with Amazon Kinesis Data Firehose for processing the high-volume event logs as stateless transactions.

B : Deploy Amazon RDS with Read Replicas for handling the shopping cart-s stateful transactions, and Amazon S3 with S3 Event Notifications to manage the stateless transactions of event logs.

C : Implement Amazon Aurora Serverless for the shopping cart data, facilitating stateful transactions with auto-scaling capabilities, and deploy Amazon EC2 instances behind an Elastic Load Balancer for processing the stateless transactions of event logging.

D : Utilize Amazon ElastiCache for Redis to manage the stateful transactions of shopping cart data, and Amazon Managed Streaming for Apache Kafka (Amazon MSK) for the high-throughput processing of stateless event log transactions.

Answer: A

Question 5

A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds

of tiles into a tact table that is in a Redshift cluster.

The company wants the data warehouse solution to achieve the greatest possible throughput. The solution

must use cluster resources optimally when the company loads data into the tact table.

Which solution will meet these requirements?

Options :

A : Use multiple COPY commands to load the data into the Redshift cluster.

B :

Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster

C :

Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.

D : Use a single COPY command to load the data into the Redshift cluster.

Answer: D

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