Available Exams | Dell Learning

Exam Number: D-AA-OP-23

Available Languages: English, French

Certification Overview

This certification is designed to build on the skills developed in Data Science Foundations and help aspiring Data Scientists continue to evolve and expand their skill sets. The main growth areas include advanced analytical methods, Hadoop (and Pig, Hive, HBase), Social Network Analysis, Natural Language Processing, and Visualization methods. The development of these skills and the use of these methods provide the data scientist the ability to identify and communicate conclusions and recommendations in order to solve business challenges across many domains.

Target Audience

Product Specialists; Solution Architects; 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
MapReduce	MapReduce framework and its implementation in Hadoop Hadoop Distributed File System (HDFS) Yet Another Resource Negotiator (YARN)	15%
Hadoop Ecosystem and NoSQL	Pig Hive NoSQL HBase Spark	15%
Natural Language Processing (NLP)	NLP and the four main categories of ambiguity Text Preprocessing Language Modeling	20%
Social Network Analysis	SNA and Graph Theory Communities Network Problems and SNA Tools	23%
Data Science Theory and Methods	Simulation Random Forests Multinomial Logistic Regression Maximum Entropy	15%
Data Visualization	Perception and Visualization Visualization of Multivariate Data	12%

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