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IBM C2090-101 valid - in .pdf
- Exam Code: C2090-101
- Exam Name: IBM Big Data Engineer
- Q & A: 108 Questions and Answers
- PDF Price: $59.99
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- Exam Code: C2090-101
- Exam Name: IBM Big Data Engineer
- Q & A: 108 Questions and Answers
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About IBM Big Data Engineer - C2090-101 exam braindumps
IBM C2090-101 Exam topics
Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our C2090-101 exam dumps will include the following topics:
- Data Preparation, Transformation, and Export: 26%
- Architecture and Integration: 17%
- Data Loading: 34%
- Data Security: 8%
- Performance and Scalability: 15%
Reference: https://www.ibm.com/certify/exam?id=C2090-101
How much C2090-101 Exam Cost
The price of the C2090-101 exam is $200 USD.
For more info visit:
What is the duration of the C2090-101 Exam
- Length of Examination: 75 minutes
- Number of Questions: 53
- Format: Multiple choices, multiple answers
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IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance and Scalability | 15% | - Tune Hive, HBase, and Big SQL performance - Optimize query execution and workload management - Apply best practices for latency and throughput - Ensure high availability and scalability |
| Topic 2: Data Preparation, Transformation, and Export | 26% | - Create BigSheets workbooks - Apply Jaql for data manipulation - Export results from Hadoop environment - Perform entity resolution and data cleansing - Use Pig for data transformation |
| Topic 3: Data Security | 8% | - Apply data masking and redaction techniques - Implement access control and LDAP security - Monitor and secure sensitive data - Comply with security standards (PCI, PII protection) |
| Topic 4: Architecture and Integration | 17% | - Evaluate use cases for Hive, Big SQL, and HBase - Implement MapReduce and RDD concepts - Design cluster architecture and storage layers - Ensure consistent processing with InfoSphere Streams - Integrate GPFS and distributed file systems |
| Topic 5: Data Loading | 34% | - Load log files with Flume and command-line tools - Import streaming data using InfoSphere Streams - Load unstructured data into InfoSphere BigInsights - Ingest data into HBase and Hive - Create Big SQL tables and load data into Hadoop - Extract data from relational sources using Sqoop |
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