C2090-102 - IBM Big Data Architect Practice Exam Questions - RETIRED
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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