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所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-certified-big-data-specialty-bds-c00-test-paper/
课程评论:没有评论
课程名称:AWS Certified Big Data - (专精 (BDS-C00) 测试题) 课程概述:本课程旨在帮助学员备考 AWS Certified Big Data - Specialty 认证,涵盖处理大数据环境时所需的关键概念及最佳实践。课程内容围绕多个真实场景问题展开,涉及数据处理平台架构设计、数据分析算法实施及AWS服务的有效使用。 示例问题包括但不限于以下几个方面: 1. 在制造公司中,数据工程师需要设计一个数据处理平台,以高效填充 Amazon Redshift 的星型模式。有效的架构策略问题涉及如何使用 Amazon EMR、S3 事件通知与 AWS Lambda 进行数据转换和插入。 2. 在处理 SPAM 邮件的分类算法时,如何选择最佳的 AWS 服务策略以便对存储在 S3 中的庞大数据集进行分析。问题探讨了使用 Amazon EMR、ElastiCache 和 AWS Data Pipeline 的优缺点。 3. 针对合规性需求,如何提供安全控制框架以映射 AWS 的责任和控制措施,问题涉及利用 AWS 的审计报告和架构设计来确保合规性。 4. 在 Redshift 集群中,设计星型模式的分发策略时,如何选择最佳的 Key-based 分发方式,问题提示选择关键节点来优化数据分配及共置数据的情境。 5. 在处理大型历史购买订单数据集时,如何满足法律要求并有效地在 AWS 云中进行数据聚类,涵盖了数据匿名化、直接连接、数据加密及使用 AWS Import/Export Snowball 设备的方案。 本课程通过理论学习与实际场景应用的结合,帮助学员掌握AWS大数据处理与分析的核心知识,以顺利通过认证考试。
Sample Questions:Q) 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?A. Transform the unstructured data using Amazon EMR and generate CSV data. COPY the CSV data into the analysis schema within Redshift.B. Load the unstructured data into Redshift, and use string parsing functions to extract structured data for inserting into the analysis schema.C. 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.D. 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.Q) 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?A. Copy the data into Amazon Elasti Cache to perform text analysis on the in-memory data and export the results of the model into Amazon Machine Learning.B. Use Amazon EMR to parallelize the text analysis tasks across the cluster using a streaming program step.C. Use Amazon Elasticsearch Service to store the text and then use the Python Elasticsearch Client to run analysis against the text index.D. Initiate a Python job from AWS Data Pipeline to run directly against the Amazon S3 text files.Q) 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?A. Request AWS third-party audit reports and/or the AWS quality addendum and map the AWS responsibilities to the controls that must be provided.B. Request data center Temporary Auditor access to an AWS data center to verify the control mapping.C. Request relevant SLAs and security guidelines for Amazon DynamoDB and define these guidelines within the application's architecture to map to the control framework.D. Request Amazon DynamoDB system architecture designs to determine how to map the AWS responsibilities to the control that must be provided.Q) 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.)A. When the administrator needs to optimize a large, slowly changing dimension table.B. When the administrator needs to reduce cross-node traffic.C. When the administrator needs to optimize the fact table for parity with the number of slices.D. When the administrator needs to balance data distribution and collocation data.E. When the administrator needs to take advantage of data locality on a local node for joins and aggregates.Q) 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?A. 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.B. 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.C. 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.D. 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.