CompTIA Data+ (DA0-002) — Lesson 2: Acquisition, Profiling, Cleansing, Transformation, and Quality

Learning Objectives

• Explain the roles, prerequisites, dependencies, and decision points shaping acquisition, profiling, cleansing, transformation, and quality.
• Complete the workflow and produce prepared dataset and quality exception log that another practitioner can review.
• Resolve “a duplicate-removal rule deletes valid repeat transactions” through a controlled and evidence-led troubleshooting process.

Detailed Topics

• data sources
• extract and load methods
• profiling
• missing values
• duplicates
• normalization
• transformation logic
• quality thresholds
• Security, performance, resilience, rollback, documentation, and support considerations for acquisition, profiling, cleansing, transformation, and quality

Instructor Demonstration

The instructor demonstrates the complete acquisition, profiling, cleansing, transformation, and quality workflow, records each material checkpoint in prepared dataset and quality exception log, and contrasts the accepted state with a controlled condition where a duplicate-removal rule deletes valid repeat transactions.

Hands-On Exercise

Implement or analyze Acquisition, Profiling, Cleansing, Transformation, and Quality in a controlled environment. Establish a baseline, reproduce or examine “a duplicate-removal rule deletes valid repeat transactions,” isolate the proven cause, apply a reversible correction, and retain the accepted outcome in prepared dataset and quality exception log.

1. Confirm scope, access, dependencies, expected behavior, and safety constraints.
2. Execute the planned workflow while preserving configuration and operational checkpoints.
3. Test an expected path, a boundary condition, and the identified failure or exception.
4. Retest after correction and document rollback, ownership, escalation, and preventive action.

Scenario Analysis

• Identify the requirement with the greatest influence on acquisition, profiling, cleansing, transformation, and quality.
• Compare two valid implementation or recovery choices using impact and evidence quality.
• Explain how the prepared dataset and quality exception log should be retained and transferred to the operational owner.

Troubleshooting Guidance

Start with effective configuration, recent changes, identities, connectivity, health, logs, and timing. Use prepared dataset and quality exception log to locate the earliest divergence between intended and observed behavior. Treat “a duplicate-removal rule deletes valid repeat transactions” as a hypothesis until the corrected path passes the original test and a relevant boundary test.

Validation Checklist

• The implemented state for acquisition, profiling, cleansing, transformation, and quality is traceable to approved requirements and inputs.
• The prepared dataset and quality exception log contains baseline, test, exception, correction, and accepted-result evidence.
• Security, data handling, capacity, performance, resilience, downstream impact, and rollback were considered where relevant.
• Monitoring, escalation, recovery, documentation, and support ownership are clear.

Lesson Outcome

Learners can apply Acquisition, Profiling, Cleansing, Transformation, and Quality, explain the decisions made, and defend the result with prepared dataset and quality exception log.