We have got your back for exam success | 30% sitewide discount |Use TOGETHER at checkout

C2090-101 - IBM Big Data Engineer Practice Exam Questions

C2090-101 - IBM Big Data Engineer

About IBM Big Data Engineer

This certification is intended for IBM Big Data Engineers. The Big Data Engineer works directly with the Data Architect and hands-on Developers to convert the architect's Big Data vision and blueprint into a Big Data reality. The Data Engineer possesses a deep level of technical knowledge and experience across a wide array of products and technologies. 


Prerequisite for the exam

Understand the data layer and particular areas of potential challenge/risk in the data layer

Ability to translate functional requirements into technical specifications.

Ability to take overall solution/logical architecture and provide physical architecture.

Understand Cluster Management

Understand Network Requirements

Understand Important interfaces

Understand Data Modeling

Ability to identify/support non-functional requirements for the solution

Understand Latency

Understand Scalability

Understand High Availability

Understand Data Replication and Synchronization

Understand Disaster Recovery

Understand Overall performance (Query Performance, Workload Management, Database Tuning)

Propose recommended and/or best practices regarding the movement, manipulation, and storage of data in a big data solution (including, but not limited to:

Understand Data ingestion technical options

Understand Data storage options and ramifications (for example , understand the additional requirements and challenges introduced by data in the cloud)

Understand Data querying techniques & availability to support analytics

Understand Data lineage and data governance

Understand Data variety (social, machine data) and data volume

Understand/Implement and provide guidance around data security to support implementation, including but not limited to:

  • Understand LDAP Security
  • Understand User Roles/Security
  • Understand Data Monitoring
  • Understand Personally Identifiable Information (PII) Data Security considerations


Course Outline

1. Data Loading


Load unstructured data into InfoSphere BigInsights


Import streaming data into Hadoop using InfoSphere Streams


Create a BigSheets workbook


Import data into Hadoop and create Big SQL table definitions


Import data to HBase


Import data to Hive


Use Data Click to load from relational sources into InfoSphere BigInsights with a self-service process


Extract data from a relational source using Sqoop


Load log data into Hadoop using Flume


Insert data via IBM General Parallel File System (GPFS) Posix file system API


Load data with Hadoop command line utility


2. Data Security


Keep data secure within PCI standards


Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data


3. Architecture and Integration


Implement MapReduce


Evaluate use cases for selecting Hive, Big SQL, or HBase


Create and/or query a Solr index


Evaluate use cases for selecting potential file formats (e.g. JSON, CSV, Parquet, Sequence, etc..)


Utilize Apache Hue for search visualization


4. Performance and Scalability


Use Resilient Distributed Dataset (RDD) to improve MapReduce performance


Choose file formats to optimize performance of Big SQL, JAQL, etc.


Make specific performance tuning decisions for Hive and HBase


Analyze performance considerations when using Apache Spark


5. Data Preparation, Transformation, and Export


Use Jaql query methods to transform data in InfoSphere BigInsights


Capture and prep social data for analytics


Integrating SPSS model scoring in InfoSphere Streams


Implement entity resolution within a Big Data platform (e.g. Big Match)


Utilize Pig for data transformation and data manipulation


Use Big SQL to transform data in InfoSphere BigInsights


Export processing results out of Hadoop (e.g. DataClick, DataStage, etc.)


Utilize consistent regions in InfoSphere Streams to ensure at least once processing


Exam Pattern 

  • Exam Name: IBM Big Data Engineer 
  • Exam Code: C2090-101
  • Length of Time:  75 Minutes


What do we offer?

Full-Length Mock Test with unique questions in each test set

Practice objective questions with section-wise scores

An in-depth and exhaustive explanation for every question

Reliable exam reports evaluating strengths and weaknesses

Latest Questions with an updated version

Tips & Tricks to crack the test

Unlimited access


What are our Practice Exams?

Practice exams have been designed by professionals and domain experts that simulate real time exam scenario.

Practice exam questions have been created on the basis of content outlined in the official documentation.

Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.

Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.

You can also create your own practice exam based on your choice and preference 


100% Assured Test Pass Guarantee

We have built the TestPrepTraining Practice exams with 100% Unconditional and assured Test Pass Guarantee! 


If you are not able to clear the exam, you can ask for a 100% refund.

Tags: C2090-101 - IBM Big Data Engineer Practice Exam Questions