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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: | DOWNLOAD DEMO |
| 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 |
|---|---|
| Topic 1: IBM Big Data Ecosystem | - IBM InfoSphere components and data integration tools - IBM BigInsights overview and Hadoop ecosystem integration - IBM Streams for real-time analytics |
| Topic 2: Analytics and Data Processing | - Data mining and statistical analysis concepts - Machine learning workflow basics - Descriptive, predictive, and prescriptive analytics |
| Topic 3: Big Data Architecture | - Distributed storage concepts (HDFS and object storage principles) - Data ingestion pipelines and integration patterns - Processing frameworks (batch and streaming paradigms) |
| Topic 4: Big Data Fundamentals | - Characteristics of big data (volume, velocity, variety, veracity) - Traditional data systems vs distributed data systems |
| Topic 5: Data Governance and Security | - Data privacy, compliance, and access control principles - Data governance frameworks and metadata management |
Your C2090-136 Exam Questions, Answered
What is the C2090-136 exam all about?
The IBM Foundations of IBM Big Data & Analytics Architecture V1 exam (code: C2090-136) is the official IBM exam that leads to the IBM Big Data & Analytics Foundations certification. It sits at the Associate level of the IBM certification track. It is also connected with related credentials such as IBM Data Science certifications, IBM Big Data Engineer certifications. Passing it proves to employers that your skills have been validated by IBM itself, which is why the C2090-136 credential keeps showing up in job postings.
Are there any prerequisites for the C2090-136 exam?
According to IBM, the following applies: No formal prerequisites, but familiarity with data management and analytics concepts is recommended.. Certification policies do change from time to time, so confirm the latest requirements on the official exam page at https://www.ibm.com/certify before you register.
How do I register for the C2090-136 exam?
You can book your IBM Foundations of IBM Big Data & Analytics Architecture V1 exam through the official channels below:
Depending on availability in your region, the exam is delivered as Computer-based exam (online proctored or test center depending on region and IBM testing partner).
What official training does IBM recommend for the C2090-136 exam?
IBM lists the following training options for IBM Foundations of IBM Big Data & Analytics Architecture V1 candidates:
Official courses build a solid foundation, and pairing them with the 60 practice questions from Dumpkiller shows you how ready you really are before you spend money on the exam itself.
Can I try the C2090-136 practice questions before buying?
Yes. Dumpkiller offers a free C2090-136 PDF demo so you can review the question style, difficulty, and explanations before committing to anything. After purchase, your IBM Foundations of IBM Big Data & Analytics Architecture V1 material includes 365 days of free updates, and if your product expires after that, you can extend the update service at a 50% discount from your member zone.
What happens if I do not pass the C2090-136 exam, and how is my order delivered?
If you take the corresponding C2090-136 exam within 60 days of your purchase and do not pass, you can apply for a full refund under our 100% Money Back Guarantee, subject to a few conditions: the failed exam must be the one matching your purchase; sitting the exam within 3 days of purchase does not qualify, since that leaves too little preparation time; downloading the material without actually taking the exam does not qualify; free materials and expired orders are excluded; and the candidate name must match the payer name. To apply, send a scanned copy of your enrollment slip together with your official Score Report (PDF) within 2 days after the exam, and claims are processed within 7 days. If you would rather not take a refund, you can exchange your purchase for two free products of equal value while keeping the update service on the product you originally bought. As for delivery, everything is an instant download: your products are sent to your email within one minute of payment — contact customer service if nothing arrives within 2 hours — and there is no limit on the number of computers you can install the software on.
What topics are covered in the C2090-136 exam?
The official IBM Foundations of IBM Big Data & Analytics Architecture V1 syllabus is organized into 5 main domains. The first three are Data Governance and Security, Big Data Fundamentals, and Analytics and Data Processing. For the full domain-by-domain breakdown, see the complete Exam Topics outline above.
IBM Foundations of IBM Big Data & Analytics Architecture V1 Sample Questions:
Question #1
A construction supply company has a lot of machine data ranging from sensors to GPS readings. They
want to integrate and analyze this data to help them optimize their resource allocation and gain real time
maintenance decision support while ensuring customer satisfaction. Which use case examples should the
Solution Advisor review to guide them on their Big Data & Analytics adoption planning?
A. Data Warehouse Modernization
B. Enhanced 360 View of the Customer
C. Big Data Exploration
D. Operational Analysis
Question #2
What are three major types of analytics?
A. Predictive
B. Descriptive
C. Prescriptive
D. Scorecards
E. Data Warehouse
F. Dashboards
Question #3
Risk management is an important activity for most organizations today. Reducing operational risk may
require setting up barriers which may reduce business activities and hurt profitability. How can Big Data &
Analytics provide solutions that reduce risk while maintaining profitability?
A. Big data can support company-wide integrated data governance policies and procedures.
B. Predictive analytics can prevent in-flight transaction credit card fraud.
C. Security/Intelligence Extension relies entirely on security technologies such as events. logs, and alerts.
D. Focused and detailed fraud policy in each major department of a firm is more effective than an
integrated, therefore slow, performing solution.
Question #4
A Solution Advisor is requested to attend a new customer discovery call to support their Big Data &
Analytics thinking and planning. The only information she is initially provided is the company background,
how they support their industry, their purchasing history for hardware and software, and their general
interest in doing more with information analysis. What is the typical first step that she should discuss with
the customer?
A. Identify their business objectives and use cases for Big Data & Analytics.
B. Find out which competitors they are using or considering for Big Data & Analytics.
C. Discuss their current product installation and look for cross sell potential for Big Data & Analytics
solutions.
D. Identify their frustrations and gaps with their current product usage for Big Data & Analytics.
Question #5
Which statement is true about governance of big data?
A. Information integration, data lineage, security, privacy, and master data management are key to long
term success of big data analytics.
B. Information integration is needed to correlate new unstructured data with existing data sources which
are already strictly governed.
C. Big data is all about exploration and discovery done in test and sandbox systems which do not need to
be methodically governed.
D. The big data domain is mostly limited to analyzing content generated outside the enterprise so strict
governance is not critical.
Solutions:
| Question #1 Answer: A | Question #2 Answer: A,D,F | Question #3 Answer: A | Question #4 Answer: C | Question #5 Answer: B |
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