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Evidence Guide: BSBXBD402 - Test big data samples

Student: __________________________________________________

Signature: _________________________________________________

Tips for gathering evidence to demonstrate your skills

The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!

From the Wiki University

 

BSBXBD402 - Test big data samples

What evidence can you provide to prove your understanding of each of the following citeria?

Validate assembled or obtained big data sample

  1. Establish a sampling strategy for big data testing and identify a representative sample for big data testing
  2. Assemble or obtain sample of raw big data according to legislative requirements and organisational policies and procedures
  3. Validate big data sample from various sources to ensure that big data is correct
Establish a sampling strategy for big data testing and identify a representative sample for big data testing

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Assemble or obtain sample of raw big data according to legislative requirements and organisational policies and procedures

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Validate big data sample from various sources to ensure that big data is correct

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Validate big data sample process and business logic

  1. Align datasets to relevant parts of the organisation
  2. Implement data aggregation and segregation rules on a small set of sample data and datasets
  3. Consult with required personnel to clarify and resolve identified anomalies
  4. Conduct performance testing for data throughput, data processing and sub-component performance
Align datasets to relevant parts of the organisation

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Implement data aggregation and segregation rules on a small set of sample data and datasets

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Consult with required personnel to clarify and resolve identified anomalies

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Conduct performance testing for data throughput, data processing and sub-component performance

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Validate output of captured big data sample and record results

  1. Design, formulate and select suitable test scenarios and test cases to validate output of big data sample
  2. Implement selected test scenarios and test cases with big data sample using common testing tools and according to organisational procedures
  3. Isolate sub-standard data and correct data acquisition paths as required
  4. Generate and store results of validation activity and associated supporting evidence according to organisational policies and procedures, and legislative requirements
Design, formulate and select suitable test scenarios and test cases to validate output of big data sample

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Implement selected test scenarios and test cases with big data sample using common testing tools and according to organisational procedures

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Isolate sub-standard data and correct data acquisition paths as required

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Generate and store results of validation activity and associated supporting evidence according to organisational policies and procedures, and legislative requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Optimise big data sample results and documentation

  1. Perform data cleansing on big data sample following testing according to industry practices and organisational procedures
  2. Collate validated output of testing, confirming absence of big data corruption in sample
  3. Recommend configuration optimisation changes based on performance testing results
  4. Communicate final sample results to required personnel
Perform data cleansing on big data sample following testing according to industry practices and organisational procedures

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Collate validated output of testing, confirming absence of big data corruption in sample

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Recommend configuration optimisation changes based on performance testing results

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Communicate final sample results to required personnel

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Assessed

Teacher: ___________________________________ Date: _________

Signature: ________________________________________________

Comments:

 

 

 

 

 

 

 

 

Instructions to Assessors

Required Skills and Knowledge

The candidate must demonstrate the ability to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including evidence of the ability to:

test two different big data samples: one transactional and one non-transactional

conduct performance testing on two different big data samples: one transactional and one non-transactional.

The candidate must be able to demonstrate knowledge to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including knowledge of:

legislative requirements relating to testing big data sources, including data protection and privacy laws and regulations

industry protocols and procedures required to write queries and scripts for big data testing

organisational policies and procedures relating to testing big data sources, including:

assembling and obtaining raw big data

performing data cleansing following extract, transform and load (ETL) testing

isolating sub-standard data and correcting data acquisition paths

quality assuring output

testing transactional and non-transactional sources of big data

storing test results and associated support evidence

big data validation protocols, including:

big data testing methodologies

test scripting

features and formats of common big data sources, including:

batched

real time

interactive

protocols and techniques for:

performance testing big data throughput

processing and reporting issues.