Description

Type – Exam Format

No. of Questions – 84

Access – Immediate

MCQ and Answers with Explanations

Last Update – 9th July of 2021

IBM Big Data Engineer (C2090-101)

This certification is intended for IBM Big Data Engineers. The Big Data Engineer works directly with the Data Architect and hands-on Developers to convert the architect’s Big Data vision and blueprint into a Big Data reality. The Data Engineer possesses a deep level of technical knowledge and experience across a wide array of products and technologies.

Prerequisite for the exam

• Understand the data layer and particular areas of potential challenge/risk in the data layer

• Ability to translate functional requirements into technical specifications.

• Ability to take overall solution/logical architecture and provide physical architecture.

• Understand Cluster Management

• Understand Network Requirements

• Understand Important interfaces

• Understand Data Modeling

• Ability to identify/support non-functional requirements for the solution

• Understand Latency

• Understand Scalability

• Understand High Availability

• Understand Data Replication and Synchronization

• Understand Disaster Recovery

• Understand Overall performance (Query Performance, Workload Management, Database Tuning)

• Propose recommended and/or best practices regarding the movement, manipulation, and storage of data in a big data solution (including, but not limited to:

• Understand Data ingestion technical options

• Understand Data storage options and ramifications (for example, understand the additional requirements and challenges introduced by data in the cloud)

• Understand Data querying techniques & availability to support analytics

• Understand Data lineage and data governance

• Understand Data variety (social, machine data) and data volume

• Understand/Implement and provide guidance around data security to support implementation, including but not limited to:

• Understand LDAP Security
• Understand User Roles/Security
• Understand Data Monitoring
• Understand Personally Identifiable Information (PII) Data Security considerations

Course Outline

• Data Loading
• Data Security
• Architecture and Integration
• Performance and Scalability
• Data Preparation, Transformation, and Export