C2090-102 – IBM Big Data Architect Practice Exam


Type – Exam Format

No. of Questions – 64

Access – Immediate

MCQ and Answers with Explanations

Last Update – 1st April of 2022


Type – Exam Format

No. of Questions – 61

Access – Immediate

MCQ and Answers with Explanations

Last Update – 1st April of 2022

IBM Big Data Architect (C2090-102) Exam

This certification exam is designed for an individual who has deep knowledge of the relevant technologies, understands the relationship between those technologies, and how they can be integrated and combined to effectively solve any given big data business problem. This individual has the ability to design large-scale data processing systems for the enterprise and provide input on the architectural decisions including hardware and software.

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

  • Requirements
  • Use Cases
  • Applying Technologies
  • Recoverability
  • Infrastructure


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