This exam focuses on the role of a data engineer in successful analytic projects and the various tools and techniques including SQL, NoSQL, the Hadoop ecosystem, Apache Spark, data governance, streaming and IoT data processing, Python, and building data pipelines.
Dell Technologies provides free practice tests to assess your knowledge in preparation for the exam. Practice tests allow you to become familiar with the topics and question types you will find on the proctored exam. Your results on a practice test offer one indication of how prepared you are for the proctored exam and can highlight topics on which you need to study and train further. A passing score on the practice test does not guarantee a passing score on the certification exam
Dell Technologies Data Engineering Optimize Exam Summary:
| Exam Name | Dell Technologies Certified Data Engineering Optimize |
| Exam Code | D-DS-OP-23 |
| Exam Price | $230 (USD) |
| Duration | 90 mins |
| Number of Questions | 60 |
| Passing Score | 63% |
| Books / Training | Data Scientist and Data Engineering Optimize Training |
| Sample Questions | Dell Technologies Data Engineering Optimize Sample Questions |
| Practice Exam | Dell Technologies D-DS-OP-23 Certification Practice Exam |
Dell Technologies D-DS-OP-23 Exam Syllabus Topics:
| Topic | Details | Weights |
| 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 |
05% |
| 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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