Use this quick start guide to collect all the information about IBM Big Data Engineer (C2090-101) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the C2090-101 IBM Big Data Engineer exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual IBM Big Data Engineer certification exam.
The IBM Big Data Engineer certification is mainly targeted to those candidates who want to build their career in IBM Data and AI - Platform Analytics domain. The IBM Certified Data Engineer - Big Data exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of IBM Big Data Engineer.
IBM Big Data Engineer Exam Summary:
| Exam Name | IBM Certified Data Engineer - Big Data |
| Exam Code | C2090-101 |
| Exam Price | $200 (USD) |
| Duration | 75 mins |
| Number of Questions | 53 |
| Passing Score | 64% |
| Books / Training | IBM Knowledge Center |
| Schedule Exam | Pearson VUE |
| Sample Questions | IBM Big Data Engineer Sample Questions |
| Practice Exam | IBM C2090-101 Certification Practice Exam |
IBM C2090-101 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Data Loading |
- Load unstructured data into InfoSphere BigInsights - Import streaming data into Hadoop using InfoSphere Streams - Create a BigSheets workbook - Import data into Hadoop and create Big SQL table definitions - Import data to HBase - Import data to Hive - Use Data Click to load from relational sources into InfoSphere BigInsights with a self-service process - Extract data from a relational source using Sqoop - Load log data into Hadoop using Flume - Insert data via IBM General Parallel File System (GPFS) Posix file system API - Load data with Hadoop command line utility |
34% |
| Data Security |
- Keep data secure within PCI standards - Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data |
8% |
| Architecture and Integration |
- Implement MapReduce - Evaluate use cases for selecting Hive, Big SQL, or HBase - Create and/or query a Solr index - Evaluate use cases for selecting potential file formats (e.g. JSON, CSV, Parquet, Sequence, etc..) - Utilize Apache Hue for search visualization |
17% |
| Performance and Scalability |
- Use Resilient Distributed Dataset (RDD) to improve MapReduce performance - Choose file formats to optimize performance of Big SQL, JAQL, etc. - Make specific performance tuning decisions for Hive and HBase - Analyze performance considerations when using Apache Spark |
15% |
| Data Preparation, Transformation, and Export |
- Use Jaql query methods to transform data in InfoSphere BigInsights - Capture and prep social data for analytics - Integrating SPSS model scoring in InfoSphere Streams - Implement entity resolution within a Big Data platform (e.g. Big Match) - Utilize Pig for data transformation and data manipulation - Use Big SQL to transform data in InfoSphere BigInsights - Export processing results out of Hadoop (e.g. DataClick, DataStage, etc.) - Utilize consistent regions in InfoSphere Streams to ensure at least once processing |
26% |
To ensure success in IBM Big Data Engineer certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for IBM Big Data Engineer (C2090-101) exam.