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C2090-102 - IBM Big Data Architect Practice Exam Questions

C2090-102 - IBM Big Data Architect

About IBM Big Data Architect

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

1. Requirements


Define the input data structure


Define the outputs


Define the security requirements


Define the requirements for replacing and/or merging with existing business solutions


Define the solution to meet the customer's SLA


Define the network requirements based on the customer's requirements


2. Use Cases


Determine when a cloud based solution is more appropriate vs. in-house (and migration plans from one to the other)


Demonstrate why Cloudant would be an applicable technology for a particular use case


Demonstrate why SQL or NoSQL would be an applicable technology for a particular use case


Demonstrate why Open Data Platform would be an applicable technology for a particular use case


Demonstrate why BigInsights would be an applicable technology for a particular use case


Demonstrate why BigSQL would be an applicable technology for a particular use case


Demonstrate why Hadoop would be an applicable technology for a particular use case


Demonstrate why BigR and SPSS would be an applicable technology for a particular use case


Demonstrate why BigSheets would be an applicable technology for a particular use case


Demonstrate why Streams would be an applicable technology for a particular use case


Demonstrate why Netezza would be an applicable technology for a particular use case


Demonstrate why DB2 BLU would be an applicable technology for a particular use case


Demonstrate why GPFS/HPFS would be an applicable technology for a particular use case


Demonstrate why Spark would be an applicable technology for a particular use case


Demonstrate why YARN would be an applicable technology for a particular use case


3. Applying Technologies


Define the necessary technology to ensure horizontal and vertical scalability


Determine data storage requirements based on data volumes


Design a data model and data flow model that will meet the business requirements


Define the appropriate Big Data technology for a given customer requirement (e.g. Hive/HBase or Cloudant)


Define appropriate storage format and compression for given customer requirement


4. Recoverability


Define the potential need for high availability


Define the potential disaster recovery requirements


Define the technical requirements for data retention


Define the technical requirements for data replication


Define the technical requirements for preventing data loss


5. Infrastructure 


Define the hardware and software infrastructure requirements


Design the integration of the required hardware and software components


Design the connectors / interfaces / API's between the Big Data solution and the existing systems


Exam Pattern 

  • Exam Name: IBM Big Data Architect
  • Exam Code: C2090-102
  • Length of Time:  90 Minutes


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