AWS Certified Big Data - Specialty

Here you have the best Amazon BDS-C00 practice exam questions

You have 85 total questions to study from
Each page has 5 questions, making a total of 17 pages
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This questions were last updated on November 17, 2024

Question 1 of 85

A data engineer in a manufacturing company is designing a data processing platform that receives a large volume of unstructured data. The data engineer must populate a well-structured star schema in Amazon

Redshift.

What is the most efficient architecture strategy for this purpose?

Transform the unstructured data using Amazon EMR and generate CSV data. COPY the CSV data into the analysis schema within Redshift.

Load the unstructured data into Redshift, and use string parsing functions to extract structured data for inserting into the analysis schema.

When the data is saved to Amazon S3, use S3 Event Notifications and AWS Lambda to transform the file contents. Insert the data into the analysis schema on Redshift.

Normalize the data using an AWS Marketplace ETL tool, persist the results to Amazon S3, and use AWS Lambda to INSERT the data into Redshift.

Correct Answer: A

Transforming the unstructured data using Amazon EMR and generating CSV data is the most efficient architecture strategy. This approach allows for the powerful processing capabilities of EMR to handle large volumes of unstructured data and turn it into a well-structured format (CSV). Once the data is in CSV format, it can be efficiently loaded into Amazon Redshift using the COPY command, which is optimized for bulk data ingestion. This method avoids the performance and complexity issues inherent in loading unstructured data directly into Redshift or attempting to transform it within a Lambda function.

Question 2 of 85

A new algorithm has been written in Python to identify SPAM e-mails. The algorithm analyzes the free text contained within a sample set of 1 million e-mails stored on Amazon S3. The algorithm must be scaled across a production dataset of 5 PB, which also resides in Amazon S3 storage.

Which AWS service strategy is best for this use case?

Copy the data into Amazon ElastiCache to perform text analysis on the in-memory data and export the results of the model into Amazon Machine Learning.

Use Amazon EMR to parallelize the text analysis tasks across the cluster using a streaming program step.

Use Amazon Elasticsearch Service to store the text and then use the Python Elasticsearch Client to run analysis against the text index.

Initiate a Python job from AWS Data Pipeline to run directly against the Amazon S3 text files.

Correct Answer: B

Amazon EMR (Elastic MapReduce) is the most appropriate choice for analyzing a massive dataset like 5 PB. Amazon EMR allows for the parallel processing of large data volumes using distributed data frameworks such as Apache Hadoop and Apache Spark. These frameworks can be used to run complex algorithms, including text analysis, in a highly scalable manner. By using EMR, the text analysis tasks can be effectively parallelized across a large cluster of instances, making it possible to handle the large scale of data efficiently. Other options like Amazon Elasticsearch Service or ElastiCache do not scale well to handle a dataset of this size.

Question 3 of 85

A data engineer chooses Amazon DynamoDB as a data store for a regulated application. This application must be submitted to regulators for review. The data engineer needs to provide a control framework that lists the security controls from the process to follow to add new users down to the physical controls of the data center, including items like security guards and cameras.

How should this control mapping be achieved using AWS?

Request AWS third-party audit reports and/or the AWS quality addendum and map the AWS responsibilities to the controls that must be provided.

Request data center Temporary Auditor access to an AWS data center to verify the control mapping.

Request relevant SLAs and security guidelines for Amazon DynamoDB and define these guidelines within the applications architecture to map to the control framework.

Request Amazon DynamoDB system architecture designs to determine how to map the AWS responsibilities to the control that must be provided.

Correct Answer: A

To provide a control framework that lists security controls for a regulated application, it is necessary to leverage AWS's independent third-party audit reports. These reports, such as AWS System and Organization Controls (SOC) Reports, demonstrate how AWS meets key compliance controls and objectives. By requesting these reports, one can map AWS's responsibilities to the necessary security controls, including both the processes for adding new users and the physical controls in data centers, like security guards and cameras. This method ensures a comprehensive and verified approach to satisfying regulatory requirements.

Question 4 of 85

An administrator needs to design a distribution strategy for a star schema in a Redshift cluster. The administrator needs to determine the optimal distribution style for the tables in the Redshift schema.

In which three circumstances would choosing Key-based distribution be most appropriate? (Select three.)

When the administrator needs to optimize a large, slowly changing dimension table.

When the administrator needs to reduce cross-node traffic.

When the administrator needs to optimize the fact table for parity with the number of slices.

When the administrator needs to balance data distribution and collocation data.

When the administrator needs to take advantage of data locality on a local node for joins and aggregates.

Correct Answer: B, D, E

Key-based distribution is most appropriate when the administrator needs to reduce cross-node traffic, balance data distribution and collocation data, and take advantage of data locality on a local node for joins and aggregates. This distribution style helps to ensure that rows with the same key value are stored on the same node, minimizing the amount of data that needs to be transferred between nodes during query execution, and thereby optimizing the performance of joins and aggregations on large datasets.

Question 5 of 85

Company A operates in Country X. Company A maintains a large dataset of historical purchase orders that contains personal data of their customers in the form of full names and telephone numbers. The dataset consists of 5 text files, 1TB each. Currently the dataset resides on-premises due to legal requirements of storing personal data in-country. The research and development department needs to run a clustering algorithm on the dataset and wants to use Elastic Map Reduce service in the closest AWS region. Due to geographic distance, the minimum latency between the on-premises system and the closet AWS region is 200 ms.

Which option allows Company A to do clustering in the AWS Cloud and meet the legal requirement of maintaining personal data in-country?

Anonymize the personal data portions of the dataset and transfer the data files into Amazon S3 in the AWS region. Have the EMR cluster read the dataset using EMRFS.

Establish a Direct Connect link between the on-premises system and the AWS region to reduce latency. Have the EMR cluster read the data directly from the on-premises storage system over Direct Connect.

Encrypt the data files according to encryption standards of Country X and store them on AWS region in Amazon S3. Have the EMR cluster read the dataset using EMRFS.

Use AWS Import/Export Snowball device to securely transfer the data to the AWS region and copy the files onto an EBS volume. Have the EMR cluster read the dataset using EMRFS.

Correct Answer: A

Anonymizing the personal data portions of the dataset and transferring the data files into Amazon S3 in the AWS region allows Company A to meet the legal requirement of maintaining personal data in-country. Anonymized data does not fall under the same legal restrictions as personal data, so moving it out of the country for processing is permissible. The research and development department can run the clustering algorithm using Amazon EMR, which will read the dataset using EMRFS from S3. This approach avoids the high latency issues that would arise from directly reading the data over the network.