Data Acquisition, Profiling and Extraction
Data acquisition questions test collecting data from databases, files, APIs, surveys, logs, and third parties, then profiling shape, completeness, and anomalies.
How to study for CompTIA Data+
Treat each Data+ item as an analytics decision: identify the source, quality issue, transformation, statistic, visual, audience, and control.
Core concepts
Concept 1
Acquisition should preserve source meaning, permissions, timing, refresh needs, and data lineage.
Exam cue: Capture source, permissions, lineage, and refresh cadence.
Concept 2
Profiling reveals nulls, duplicates, type mismatches, outliers, distributions, and quality concerns before transformation.
Exam cue: Profile completeness, uniqueness, type, distribution, and anomalies.
Concept 3
Extraction methods should match source system capacity, security, frequency, and downstream requirements.
Exam cue: Choose extraction method based on source and business need.
Risk pitfalls and guardrails
Pulling production data without permission or privacy controls.
Guardrail: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Cleaning data before profiling the original quality problem.
Guardrail: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Ignoring refresh cadence when building a recurring report.
Guardrail: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Memory anchors
Data Acquisition
Data acquisition collects data from approved sources for analysis or reporting.
API Extract
An API extract retrieves structured data from an application endpoint under access and rate limits.
Batch Extract
A batch extract moves data on a scheduled or grouped basis.
Streaming Data
Streaming data arrives continuously and supports near-real-time processing.
Data Profiling
Data profiling summarizes structure, quality, completeness, uniqueness, and distribution.
Null Rate
Null rate measures missing values in a field.
Duplicate Check
Duplicate checks identify repeated records that can distort counts or results.
Type Mismatch
A type mismatch occurs when stored values do not fit the expected data type.
Lineage
Lineage records where data came from and how it changed over time.
Refresh Cadence
Refresh cadence defines how often data is updated for downstream use.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
An analyst is asked to copy production patient data into a personal workspace for convenience. Which action is MOST appropriate? Use only the facts stated.
Canyon Transit needs only completed permits records from the last quarter and the source is a relational database. Which recommendation is BEST? Use only the facts stated.
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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