This is a multiple choice exam conducted by IBM for a BigData Architect. IBM also has Hadoop framework known as BigInsight and they will be asking Question based on BigInsight, however it is very similar to Hadoop only, because they are using Apache Hadoop framework only. As you know, IBM is the oldest and one of the matured software vendor and they have more penetration in the Industry, compare to any other BigData vendor. Hence, certifying yourself as a BigData Architect for IBM, ceratinly have high value in industry. Hence, to preapre you for this certification, HadoopExam is providing in total 240 Questions. Go through with each question as well as its explanation, close to 95% questions have in depth explanation or relevant statement , to justify the answer.. Also, before appearing in real exam, please drop an email. If there are any update with regards to the exam we have, then we will share with you. So practice these all 240 questions and clear this certification exam and become an IBM certified BigData Architect. There is no doubt that certification has higher value compare to not certified candidate.. you have heard in your organization.. this person is certified on this.. so it really matter during interview and while working in a company to have exclusive impression by having certifications. We have more material for various certifications and trainings.. check here

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Test Information :

• Number of questions: 55
• Time allowed in minutes: 90
• Required passing score: 60%
• Languages: English

Exam Objectives : There are in total 5 sections and you will be asked total 55 questions in real exam. Please check each section below with regards to the exam objective

Requirements (16%)

• Define the input data structure
• Define the outputs
• Define the security requirements
• Define the requirements for replacing and/or merging with existing business solutions
• Define the solution to meet the customer's SLA
• Define the network requirements based on the customer's requirements

Use Cases (46%)

• Determine when a cloud based solution is more appropriate vs. in-house (and migration plans from one to the other)
• Demonstrate why Cloudant would be an applicable technology for a particular use case
• Demonstrate why SQL or NoSQL would be an applicable technology for a particular use case
• Demonstrate why Open Data Platform would be an applicable technology for a particular use case
• Demonstrate why BigInsights would be an applicable technology for a particular use case
• Demonstrate why BigSQL would be an applicable technology for a particular use case
• Demonstrate why Hadoop would be an applicable technology for a particular use case
• Demonstrate why BigR and SPSS would be an applicable technology for a particular use case
• Demonstrate why BigSheets would be an applicable technology for a particular use case
• Demonstrate why Streams would be an applicable technology for a particular use case
• Demonstrate why Netezza would be an applicable technology for a particular use case
• Demonstrate why DB2 BLU would be an applicable technology for a particular use case
• Demonstrate why GPFS/HPFS would be an applicable technology for a particular use case
• Demonstrate why Spark would be an applicable technology for a particular use case
• Demonstrate why YARN would be an applicable technology for a particular use case

Applying Technologies (16%)

• Define the necessary technology to ensure horizontal and vertical scalability
• Determine data storage requirements based on data volumes
• Design a data model and data flow model that will meet the business requirements
• Define the appropriate Big Data technology for a given customer requirement (e.g. Hive/HBase or Cloudant)
• Define appropriate storage format and compression for given customer requirement

Recoverability (11%)

• Define the potential need for high availability
• Define the potential disaster recovery requirements
• Define the technical requirements for data retention
• Define the technical requirements for data replication
• Define the technical requirements for preventing data loss

Infrastructure (11%)

• Define the hardware and software infrastructure requirements
• Design the integration of the required hardware and software components
• Design the connectors / interfaces / API's between the Big Data solution and the existing systems
  

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