How to book the C2090-101 Exam
These are following steps for registering the C2090-101 exam.
Step 1: Visit to Pearson Exam Registration
Step 2: Signup/Login to Pearson VUE account
Step 3: Search for IBM C2090-101 Exam Certifications Exam
Step 4: Select Date, time and confirm with payment method
IBM C2090-101 exam is a multiple-choice exam that consists of 59 questions. Candidates have 90 minutes to complete the exam and must score at least 62% to pass. C2090-101 exam is available in English and Japanese and can be taken at an authorized testing center or online. Candidates who pass the exam will receive an IBM Certified Big Data Engineer certification, which is recognized by employers around the world as a mark of expertise in big data technology.
IBM C2090-101 certification is ideal for professionals who are looking to build a career in big data and analytics, particularly in IBM-specific tools and technologies. C2090-101 exam tests the candidate’s ability to manage data, design big data solutions, develop data analytics applications, and optimize big data workflows.
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IBM C2090-101: IBM Big Data Engineer Exam is a comprehensive exam that covers all aspects of Big Data Engineering. C2090-101 exam covers topics such as Big Data Architecture, Data Ingestion, Data Transformation, Data Storage, Data Analysis, and Data Visualization. C2090-101 exam is designed to test the candidate's knowledge and skills in all these areas and ensure that the candidate has the necessary expertise to work in the field of Big Data Engineering.
Reference: https://www.ibm.com/certify/exam?id=C2090-101
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IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Hadoop Distributed Processing | 28% | - Hadoop ecosystem
- 1. MapReduce
- 2. HDFS architecture
- 3. Cluster management
- 4. YARN
|
| Data Collection and Ingestion | 14% | - Data acquisition technologies
- 1. Kafka
- 2. Flume
- 3. Sqoop
|
| Big Data Foundations | 12% | - Big Data concepts
- 1. Volume, Velocity, Variety, Veracity
- 2. Big Data use cases
- 3. Big Data architecture fundamentals
|
| Data Governance, Security and Administration | 22% | - Operational management
- 1. Data governance
- 2. Security concepts
- 3. Monitoring and troubleshooting
- 4. Performance tuning
|
| Data Transformation and Analytics | 24% | - Data processing frameworks
- 1. Data querying and transformation
- 2. Apache Hive
- 3. Apache Pig
- 4. Apache Spark fundamentals
|