|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/databricks-certified-developer-for-spark-practice-exams/
课程评论：没有评论
课程名称：Databricks Certified Developer for Spark 实践考试 课程概述： 本课程旨在帮助您为Databricks认证开发人员考试做好充分准备，尤其侧重于Apache Spark核心概念的掌握。课程包含超过200道高度仿真的练习题，分布在4套模拟考试中，并提供详细的解答，涵盖了考试中涉及的所有关键Python和Spark主题，从Spark架构、DataFrames操作到Spark SQL查询优化。 每道练习题都附有深入的解释，帮助您在评估知识的同时，深入理解Spark的细微之处。您还将学习到针对考试内容的定制化技巧，以巩固核心概念。 通过本备考课程，您将获得以下方面的深入知识： * Spark核心组件：理解Driver、Executors、Jobs和Stages。 * DataFrame API：熟练运用DataFrame API进行数据转换和列操作。 * Spark SQL：掌握Spark SQL进行强大的数据分析和查询。 * 性能调优：通过分区、广播等技术优化作业性能。 * 部署：安全地在不同执行模式下部署Spark。 * 数据读写：高效读写JSON、Parquet、CSV格式的数据。 完成本课程的学习，您将全面掌握Spark的应用，为通过Databricks认证打下坚实基础。 目标学员： * 即将参加Databricks Certified Associate Developer for Apache Spark 3.0 (Python) 考试的考生。 * 希望验证自身技能并提升职业发展的Data Engineers。 * 希望展示Spark使用熟练度的Developers。 * 准备Spark开发人员角色或项目的Professionals。 * 希望扩展大数据技能集的Data Analysts。 * 正在构建大规模数据处理专业知识的学生。 * 任何希望成为认证Databricks开发人员的人。
Ready to become a Certified Databricks Developer but don't know where to start ? This comprehensive guide to passing the official certification is designed to help you master Apache Spark and ace the exam questions with confidence.This hands-on prep course includes over 200 realistic practice questions from 4 Custom Practice Exams and Detailed Explanations that cover all the key Python and Spark topics tested on the exam - from Spark architecture to DataFrames to Spark SQL query optimization.Every practice question comes with detailed explanations of the topic discussed. This way, you can learn the nuances of Spark while assessing your knowledge. You'll also get customised elements of the topics discusses in the exam - everything you need to reinforce the core concepts.By the end of the certification prep, you'll have in-depth knowledge of:Core Spark components like driver, executors, jobs, and stagesLeveraging DataFrame APIs for transformations and column operationsUsing SparkSQL for powerful data analysis and queryingTuning jobs for optimal performance with partitioning, broadcastingDeploying Spark securely across execution modesReading and writing data in JSON, Parquet, CSV formatsWith complete hands-on mastery over Spark, you'll be fully equipped to validate your skills through Databricks certification.Don't leave it to chance - enroll now and let our Spark experts comprehensively prepare you for success in becoming a certified Databricks developer!SAMPLE QUESTIONQuestionWhich of the following code blocks correctly writes DataFrame transactionsDf as a parquet file to the specified location filePath, partitioned by the column storeId, and with the overwrite mode using the DataFrameWriter and col() operator?transactionsDf.write.partitionBy(col("storeId")).mode("overwrite").parquet(filePath)transactionsDf.write.partitionBy("storeId").mode("overwrite").parquet(filePath)transactionsDf.write.partitionBy(col("storeId")).parquet(filePath).mode("overwrite")transactionsDf.writer.partitionBy(col("storeId")).mode("overwrite").parquet(filePath)transactionsDf.write.mode("overwrite").partitionBy(col("storeId")).parquet(filePath)Correct Answer: A. transactionsDf.write.partitionBy(col("storeId")).mode("overwrite").parquet(filePath)Explanation:partitionBy(col("storeId")): This function is used to specify the column storeId as the partitioning column for the parquet file. It ensures that the data is partitioned based on the distinct values in the storeId column.mode("overwrite"): This function is used to specify the write mode for the parquet file. In this case, "overwrite" mode is used, which means that if the file already exists at the specified filePath, it will be overwritten.parquet(filePath): This function is used to write the DataFrame as a parquet file at the specified filePath.By combining these functions, the code block correctly partitions the DataFrame on the storeId column and writes it as a parquet file to the specified location, ensuring that any existing file at that location is overwritten.WHO IS THE COURSE FOR?For those who are getting ready to ace the Databricks Certified Associate Developer for Apache Spark 3.0 exam in PythonData engineers looking to validate their skills and advance their careersDevelopers who want to demonstrate proficiency in using Apache SparkProfessionals preparing for a Spark developer role or projectData analysts seeking to expand their big data skillsetStudents building expertise in large-scale data processingAnyone interested in becoming a certified Databricks developer
