IBM C2090-136 - Foundations of IBM Big Data & Analytics Architecture V1
- Exam Code: C2090-136
- Exam Name: Foundations of IBM Big Data & Analytics Architecture V1
- Updated: Sep 22, 2026
- Q & A: 60 Questions and Answers
C2090-136 Free Demo download
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Still confused about how to prepare for the C2090-136 exam? The confusion ends with a clear resource: TestPDF's bank of 60 practice questions covering the IBM Foundations of IBM Big Data & Analytics Architecture V1 objectives, every answer expert-verified, ready in 2026.
IBM C2090-136 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | Foundations of IBM Big Data & Analytics Architecture V1 |
| Exam Number: | C2090-136 |
| Available Languages: | English |
| Related Certifications: | IBM Big Data Engineer certifications IBM Data Science certifications |
| Exam Format: | Multiple choice, Scenario-based questions |
| Recommended Training: | IBM Big Data and Analytics training courses |
| Exam Registration: | IBM Certification Portal |
| Sample Questions: |  |
| Exam Way: | Computer-based exam (online proctored or test center depending on region and IBM testing partner) |
| Pre Condition: | No formal prerequisites, but familiarity with data management and analytics concepts is recommended. |
| Official Syllabus URL: | https://www.ibm.com/certify |
IBM C2090-136 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Governance and Security | - Data governance frameworks and metadata management - Data privacy, compliance, and access control principles |
| Analytics and Data Processing | - Machine learning workflow basics - Descriptive, predictive, and prescriptive analytics - Data mining and statistical analysis concepts |
| Big Data Architecture | - Processing frameworks (batch and streaming paradigms) - Data ingestion pipelines and integration patterns - Distributed storage concepts (HDFS and object storage principles) |
| Big Data Fundamentals | - Characteristics of big data (volume, velocity, variety, veracity) - Traditional data systems vs distributed data systems |
| IBM Big Data Ecosystem | - IBM Streams for real-time analytics - IBM BigInsights overview and Hadoop ecosystem integration - IBM InfoSphere components and data integration tools |
C2090-136 Exam Information, Without the Confusion
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The IBM Foundations of IBM Big Data & Analytics Architecture V1 blueprint covers these principal domains:
- Big Data Fundamentals
- IBM Big Data Ecosystem
- Big Data Architecture
The remaining domains appear in the full official outline, all of which our bank addresses.
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IBM lists the following prerequisites for the IBM Foundations of IBM Big Data & Analytics Architecture V1: No formal prerequisites, but familiarity with data management and analytics concepts is recommended..
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IBM Foundations of IBM Big Data & Analytics Architecture V1 Sample Questions:
Question #1
Which in-memory database design point helps use main memory more efficiently?
- A. Single Instruction Multiple Data processing.
- B. Organizing data according to columns rather than rows.
- C. Processing data using multiple processor cores.
- D. Automatic and always-on data compression.
Correct Answer: A 🗳️
Question #2
Using Brewer's CAP theorem as a guide for choosing between alternative data storage technologies,
which two distributed system properties does Apache HBase (part of Hadoop) guarantee?
- A. Consistency - all nodes see the same data at the same time.
- B. Availability - every request receives a response about whether it was successful or failed
- C. Partition tolerance - the system continues to operate despite arbitrary message loss.
- D. Atomicity - if one part of a transaction fails, the entire transaction fails.
- E. Concurrency control - correct results for concurrent operations.
Correct Answer: A,D 🗳️
Question #3
Which two statements are true when considering a solution for real-time data processing?
- A. Real-time data processing within IBM InfoSphere Biglnsights can be performed with sub-second
response times using Apache Hadoop Streaming, and IBM InfoSphere Streams is included for processing
sensor-generated data. - B. Real-time data can be processed by IBM InfoSphere Streams and then optionally stored within IBM
InfoSphere Biglnsights as part of the IBM Big Data & Analytics platform. - C. Real-time analytics provided by IBM InfoSphere Streams can be used for fraud detection, health
monitoring, or machine data processing, whereas other products in IBM's Big Data & Analytics platform
are provided for sensor data and other real-time scenarios. - D. Real-time prediction and scoring can be performed by using the combination of IBM InfoSphere
Streams and SPSS products within the IBM Big Data & Analytics platform. - E. Real-time analytics can be performed within IBM InfoSphere Biglnsights by using the inmemoryquery
engine in IBM Biglnsights after data has first flowed through IBM InfoSphere Streams to capture data into
memory.
Correct Answer: B,C 🗳️
Question #4
A Solution Advisor is asked to recommend infrastructure capability to match the varying demands of a big
data application. What should be the best choice to meet the business needs?
- A. Scalability in, out. and up to meet varying demands.
- B. Best performing storage solution available like SS
- C. Parallel processing with optimized computation,
- D. High systems availability with security.
Correct Answer: A 🗳️
Question #5
Which combination of IBM software solutions delivers up to 82 times faster reporting on an IBM
POWER8-based server than a comparable x86 server?
- A. IBMDB2 Analytics Accelerator and IBM CognosPowerCubes
- B. Apache Hadoop integration with IBM InfoSphere Biglnsights and IBM InfoSphere Streams
- C. IBM SPSS on an IBM DB2 database
- D. IBM DB2 with BLU Acceleration and IBM Cognos Dynamic Cubes
Correct Answer: D 🗳️
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