When an employer sees the 000-N32 certification on a resume, it signals verified skill. DumpStillValid supports that goal with IBM Big Data Fundamentals Technical Mastery Test v1 material whose 34 practice questions are refined from a large body of technical knowledge, not copied from stale files.
IBM 000-N32 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM Big Data Fundamentals Technical Mastery Test v1 |
| Exam Number: | P2090-032 |
| Related Certifications: | IBM InfoSphere BigInsights Technical Mastery IBM Information Management Mastery |
| Certificate Validity Period: | Retired, no longer active |
| Available Languages: | French, Spanish, Japanese, English, German, Italian |
| Exam Price: | USD 100 - 200 (varies by region) |
| Exam Duration: | 75 minutes |
| Real Exam Qty: | 33-34 |
| Passing Score: | 67% |
| Exam Format: | Multiple Select, Multiple Choice |
| Recommended Training: | IBM Big Data Fundamentals Course |
| Exam Registration: | IBM Training & Certification |
| Sample Questions: |  |
| Exam Way: | Online proctored or authorized testing center (before retirement) |
| Pre Condition: | No formal prerequisites; basic knowledge of data management and IT concepts recommended |
| Official Syllabus URL: | https://www.ibm.com/training/certification |
IBM 000-N32 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| InfoSphere Data Explorer | 20% | - Federated data access and connectivity - Search and discovery functionality - Support for diverse data sources |
| InfoSphere Streams | 25% | - Architecture and use cases - Real-time streaming analytics concepts - Integration with other IBM big data solutions |
| InfoSphere BigInsights and Hadoop Ecosystem | 30% | - BigInsights editions and capabilities - Data storage and processing frameworks - Architecture and components of InfoSphere BigInsights - Integration with Apache Hadoop |
| Big Data Concepts and Characteristics | 25% | - Structured, semi-structured, and unstructured data types - Business drivers and challenges of big data - 4 Vs of Big Data: Volume, Velocity, Variety, Veracity |
Before You Buy: IBM Big Data Fundamentals Technical Mastery Test v1 FAQ
The 000-N32 exam contains 33-34 questions to be answered within 75 minutes minutes. Rehearsing with a timer on DumpStillValid trains you to pace each question instead of rushing at the end.
No formal prerequisites; basic knowledge of data management and IT concepts recommended
The IBM Big Data Fundamentals Technical Mastery Test v1 material is developed by a dedicated research team whose members bring rich hands-on IT experience, including past work for international IT corporations. That background shapes both the questions and the way they are organized for study.
Each IBM Big Data Fundamentals Technical Mastery Test v1 question is drawn from a large body of technical knowledge and analyzed before it enters the bank. Questions that are no longer relevant to the current exam are removed, while new material is added as the exam evolves, so the set you study stays lean and current.
- Big Data Concepts and Characteristics (25%)
- InfoSphere BigInsights and Hadoop Ecosystem (30%)
- InfoSphere Streams (25%)
Begin with the topics where your confidence is lowest, then confirm the rest.
The passing score for 000-N32 is 67%, and the official exam fee is USD 100 - 200 (varies by region). Fixing these numbers in mind helps you plan both your study target and your budget.
The package includes 34 practice questions for the 000-N32 exam, covering the current IBM Big Data Fundamentals Technical Mastery Test v1 objectives with verified answers.
IBM Big Data Fundamentals Course: Visit official page
IBM Training & Certification: register for 000-N32
IBM Big Data Fundamentals Technical Mastery Test v1 Sample Questions:
Which of the following toolkits is NOT provided by InfoSphere Streams?
- A. Intranet toolkit.
- B. Finance toolkit
- C. Standard toolkit.
- D. Database toolkit.
Correct Answer: A 🗳️
Which of the following options is CORRECT regarding InfoSphere Data Explorer?s annotators?
- A. InfoSphere Data Explorer?s annotators allow users to interact with search results by providing
feedback about the result's value, and by
adding useful information and communication with other users. - B. InfoSphere Data Explorer?s annotators is an add-on feature capable of handling of a variety of
data formats and types, including structured,
semi-structured and unstructured, as well as the special demands of rich media and transactional
data. - C. InfoSphere Data Explorer?s annotators allow users to create groups of search results.
- D. InfoSphere Data Explorer?s annotators allow users to save results in a private/public folder for
later review or sharing.
Correct Answer: A 🗳️
Which of the following options best describes the proper usage of MapReduce jobs in Hadoop environments?
- A. MapReduce jobs are used to process vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.
- B. MapReduce jobs are used to execute sequential search outside the Hadoop environment using a built-in UDF to access information stored in non-relational databases.
- C. MapReduce jobs are used to process small amounts of data in-parallel on expensive hardware, without fault-tolerance.
- D. MapReduce jobs are used to process structured data in sequence, with fault-tolerance.
Correct Answer: A 🗳️
How do big data solutions interact with the existing enterprise infrastructure?
- A. Big data solutions must be isolated into a separate virtualized environment optimized for sequential workloads, so that it doesn?t interact with existing infrastructure.
- B. Big data solutions must substitute for the existing enterprise infrastructure; therefore there is no interaction between them.
- C. Big data solutions works in parallel with the existing enterprise infrastructure leveraging all the unstructured raw data that cannot be processed and stored in a traditional data warehouse solutions.
- D. Big data solutions are placed on top of the existing enterprise infrastructure, acting as a transparent layer converting unstructured raw data into structured, readable data, and storing the final results in a traditional data warehouse.
Correct Answer: C 🗳️
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