Question 1

An analytics team at a retail corporation is looking to process their vast amounts of transactional data stored in Amazon S3 using complex analytical queries. The team plans to use Amazon EMR with Apache Hive for their batch processing needs. They require a metadata management system that will allow them to define, organize, and query their data structure.

What component should the team implement within their Amazon EMR environment to manage their metadata effectively and ensure compatibility with Hive?

Options :

A : Use the EMR File System (EMRFS) to handle Hive metadata alongside the transactional data in S3.

B : Deploy an Amazon RDS instance configured as an external Hive Metastore for the EMR cluster.

C : Configure the EMR cluster to use the Glue Data Catalog as a drop-in replacement for the Hive Metastore.

D : Implement Amazon DynamoDB to store and manage Hive metadata for EMR processing tasks.

Answer: C

Question 2

A company stores daily records of the financial performance of investment portfolios in .csv format in an

Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.

The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.

Which solution will meet these requirements?

Options :

A :

Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.

B :

Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Create a daily schedule to run the crawler. Specify a database name for the output.

C :

Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.

D :

Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.

Answer: B

Question 3

A digital marketing agency wants to collect, analyze, and visualize web traffic data from their diverse portfolio of client websites for enhanced marketing insights. The solution needs to handle large volumes of data, offer powerful search capabilities, and provide an interactive dashboard for visualization and analysis.

Which combination of AWS services should the agency use to collect, search, analyze, and visualize the web traffic data?

Options :

A : Use Amazon Kinesis Data Firehose for data collection, Amazon OpenSearch Service for search and analysis, and Amazon QuickSight for visualization.

B : Implement Amazon MSK for data streaming, Amazon Redshift for data analysis, and Amazon OpenSearch Service for visualization.

C : Use AWS Direct Connect for data ingestion, Amazon RDS for data storage and querying, and Amazon OpenSearch Service for visualization.

D : Set up Amazon S3 for data storage, AWS Glue for data ETL, and Amazon OpenSearch Service for search, analysis, and visualization.

Answer: A

Question 4

A Data Engineering Team needs to help the security team to deliver logs from various AWS services in their production AWS account to a centralized logging AWS account for security analysis. They plan to use Amazon Kinesis Data Streams for real-time log processing and delivery.

To ensure secure and efficient log delivery between accounts, where should the Kinesis Data Stream be deployed and what security considerations should be taken into account?

Options :

A : Set up the Kinesis Data Stream in the production account and configure it to push logs directly to the centralized logging account.

B : Implement the Kinesis Data Stream in the production account and use AWS Lambda for cross-account delivery of logs to the centralized logging account.

C : Deploy the Kinesis Data Stream in the centralized logging account and establish cross-account access for the production account to write logs to the stream.

D : Deploy Kinesis Data Streams in both accounts, using one for collecting logs and the other for receiving and processing these logs.

Answer: C

Question 5

A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored

in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

Which solution will meet this requirement MOST cost-effectively?

Options :

A :

Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.

B :

Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.

C : Use Amazon Athena Federated Query to join the data from all data sources.

D :

Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Answer: C

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