Available Exams | Dell Learning

Exam Number: D-DS-OP-23

Available Languages: English, French

Certification Overview

This certification will benefit practicing or aspiring data engineers, data scientists, data stewards, or anyone who is responsible for managing or processing data sets.

Target Audience

Cloud Architects; Cloud Consultants; Cloud Product Leads/Managers; Infrastructure Administrators; Pre-Sales Solution Architects; Product Specialists; Solution Architects; Systems Engineers; Technical Consultants

Your Journey Begins Here!

Step 1 – Topics: Identify training plan and review course topics

Step 2 – Training: Complete any recommended Training

Step 3 – Exam Preparation: Study training materials and complete practice exams

Step 4 – Exam Voucher: Request digital exam vouchers

Step 5 – Schedule: Schedule exam using exam voucher code

Step 6 – Recognition: Recognize accomplishments with your Digital Badge

Practice First, Certify Next

Practice tests are unproctored web based exams. Practice tests are not available for Achievements.

Ready to Certify? Plan Your Exam Experience

Achievement exams are web based. Skills certification Exams are proctored exams administered though Pearson Vue testing centers and OnVUE.

Exam Topics

The table below lists the topics that are included in this exam, and the approximate percentage of the exam devoted to each topic.

Category	Topics	Coverage
The Role of the Data Engineer	Describe the skills of a data engineer Describe the role of a data engineer in a data analytics project	5%
Data Warehousing with SQL and NoSQL	Describe characteristics and performance considerations of a relational database Describe relational database schemas and normalization techniques Describe use cases and features of various NoSQL tools	17%
Extract-Transform-Load (ETL) Offload with Hadoop and Spark	Describe ETL, ELT, and related schedulers Describe the Hadoop ecosystem, HDFS, and data ingestion tools Describe Apache Spark and its architecture	18%
Data Governance, Security and Privacy for Big Data	Describe data governance, key roles, and related models Describe metadata and Master Data Management Describe security considerations with Hadoop and the Cloud Describe the uses of Apache Atlas, Ranger, and Knox Describe privacy regulations and ethics	20%
Processing Streaming and IoT Data	Describe uses and application of IoT tools Describe the Apache Storm system and topology • Describe the Apache Kafka queueing system and architecture Describe Apache Spark - Streaming processing and architecture Describe Apache Flink and its architecture Describe Pravega and its storage architecture Describe EdgeX Foundry and its architecture	20%
Building Data Pipelines with Python	Describe Python, reasons to use, and its libraries Describe the use of lists, dictionaries, tuples, sets, and strings Describe the use of Apache Airflow Describe data pipeline best practices	20%

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