Assessor Resource

BSBXBD402
Test big data samples

Assessment tool

Version 1.0
Issue Date: May 2024


This unit describes the skills and knowledge required to test captured transactional and non-transactional big data samples prior to using them in the organisation. It involves assembling or obtaining raw big data, processing that big data, and testing it in a way that enables it to be used more broadly within the organisation.

It applies to those who work in a broad range of industries using data analysis techniques in the management of their day-to-day work.

No licensing, legislative or certification requirements apply to this unit at the time of publication.

You may want to include more information here about the target group and the purpose of the assessments (eg formative, summative, recognition)


Evidence Required

List the assessment methods to be used and the context and resources required for assessment. Copy and paste the relevant sections from the evidence guide below and then re-write these in plain English.

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.

Skills must be assessed in a workplace or simulated environment where conditions are typical of a work environment that uses big data.

Access is required to:

information and telecommunications equipment required to test big data sources

big data sets to be tested

industry standards, organisational procedures, and legislative requirements required to demonstrate the performance evidence.

Assessors of this unit must satisfy the requirements for assessors in applicable vocational education and training legislation, frameworks and/or standards.

Submission Requirements

List each assessment task's title, type (eg project, observation/demonstration, essay, assingnment, checklist) and due date here

Assessment task 1: [title]      Due date:

(add new lines for each of the assessment tasks)

Assessment Tasks

Copy and paste from the following data to produce each assessment task. Write these in plain English and spell out how, when and where the task is to be carried out, under what conditions, and what resources are needed. Include guidelines about how well the candidate has to perform a task for it to be judged satisfactory.

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.

Skills must be assessed in a workplace or simulated environment where conditions are typical of a work environment that uses big data.

Access is required to:

information and telecommunications equipment required to test big data sources

big data sets to be tested

industry standards, organisational procedures, and legislative requirements required to demonstrate the performance evidence.

Assessors of this unit must satisfy the requirements for assessors in applicable vocational education and training legislation, frameworks and/or standards.
Copy and paste from the following performance criteria to create an observation checklist for each task. When you have finished writing your assessment tool every one of these must have been addressed, preferably several times in a variety of contexts. To ensure this occurs download the assessment matrix for the unit; enter each assessment task as a column header and place check marks against each performance criteria that task addresses.

Observation Checklist

Tasks to be observed according to workplace/college/TAFE policy and procedures, relevant legislation and Codes of Practice Yes No Comments/feedback
Establish a sampling strategy for big data testing and identify a representative sample for big data testing 
Assemble or obtain sample of raw big data according to legislative requirements and organisational policies and procedures 
Validate big data sample from various sources to ensure that big data is correct 
Align datasets to relevant parts of the organisation 
Implement data aggregation and segregation rules on a small set of sample data and datasets 
Consult with required personnel to clarify and resolve identified anomalies 
Conduct performance testing for data throughput, data processing and sub-component performance 
Design, formulate and select suitable test scenarios and test cases to validate output of big data sample 
Implement selected test scenarios and test cases with big data sample using common testing tools and according to organisational procedures 
Isolate sub-standard data and correct data acquisition paths as required 
Generate and store results of validation activity and associated supporting evidence according to organisational policies and procedures, and legislative requirements 
Perform data cleansing on big data sample following testing according to industry practices and organisational procedures 
Collate validated output of testing, confirming absence of big data corruption in sample 
Recommend configuration optimisation changes based on performance testing results 
Communicate final sample results to required personnel 

Forms

Assessment Cover Sheet

BSBXBD402 - Test big data samples
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I declare that the assessment tasks submitted for this unit are my own work.

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Result: Competent Not yet competent

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Assessment Record Sheet

BSBXBD402 - Test big data samples

Student name:

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Assessment task 1: [title] Result: Competent Not yet competent

(add lines for each task)

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Overall assessment result: Competent Not yet competent

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