CCDM SCDM Certified Clinical Data Manager Free Practice Exam Questions (2025 Updated)

Database auditsare conducted to ensureongoing data accuracy, completeness, and compliancethroughout the lifecycle of a clinical trial. According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control), quality audits are most effective when performedperiodically during study conduct, rather than waiting until study completion.

Performing audits periodically allows early detection of data entry errors, protocol deviations, and system inconsistencies, thereby reducing the risk of large-scale data issues before database lock. This proactive approach aligns withrisk-based quality management principlesoutlined inICH E6(R2)and ensures corrective actions are implemented in real time.

Options A and B represent reactive quality control, which occurs too late to prevent data issues. Option C (after first few cases) provides initial validation but does not ensure continuous oversight.

Therefore,option D — “Periodically throughout the study”— represents the optimal and compliant timing for quality audits of the database.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 5.3 – Ongoing Quality Control and Auditing

ICH E6(R2) GCP, Section 5.1.1 – Quality Management System and Risk-Based Monitoring

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Review and Auditing Practices

When efficacy data arenot covered by existing CDISC SDTM domains, thefirst resourcethe Data Manager should consult is theCDISC Therapeutic Area Implementation Guide (TAIG)for that therapeutic field.

According to theGCDMP (Chapter: Standards and Data Mapping), CDISC’s Therapeutic Area User Guides (TAUGs) and Implementation Guides providestandardized data structures, variable definitions, controlled terminology, and implementation examplesfor specific diseases or therapeutic areas. These guides ensure consistency across studies, promote interoperability, and align data collection with regulatory submission expectations.

Consulting other sponsors’ forms or external registries (options A and C) can be informative but do not provide authoritative CDISC-compliant standards. SNOMED terms (option B) address medical terminology, not structural data domain definitions.

Therefore,Option Dis correct—CDISC TA Implementation Guidesare the recognized primary reference when extending or designing SDTM-compliant CRFs.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 4.2 – Use of CDISC Standards

CDISC Therapeutic Area User Guides (TAUGs) – Implementation Guidance for Domain Extension

FDA Data Standards Catalog – CDISC Therapeutic Area Standards

TheInformed Consent Form (ICF)is the document that explicitly describes what study subjects can expect regardingdata disclosure, privacy, and confidentialityduring and after participation in a clinical trial. According toICH E6 (R2) Good Clinical PracticeandFDA Human Subject Protection Regulations (21 CFR Parts 50 and 56), participants must be fully informed about how their personal and clinical data will be collected, used, stored, and shared — both during the study and in any subsequent data-sharing or publication activities.

TheGCDMPreiterates that clinical data managers must ensure that all data handling practices align with the privacy commitments made in the ICF. This includes compliance withdata protection regulationssuch as HIPAA (in the U.S.) and GDPR (in the EU). The ICF defines the permissible scope of data use, ensuring ethical management and subject protection.

Documents like theprotocolordata sharing planmay outline procedures and responsibilities but do not directly inform participants of their rights and data use expectations. Only theICFis designed for that ethical communication purpose.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Ethics, Privacy, and Data Security

ICH E6 (R2) Good Clinical Practice, Sections 4.8.10 & 4.8.12

FDA 21 CFR Part 50 – Protection of Human Subjects, Informed Consent Requirements

Duringdata reconciliationbetween a central laboratory and CRF data, the source of truth is typically thecentral lab database, as it provides directly measured, vendor-generated results.

When thecentral lab has data but the CRF does not (option C), the Data Manager must first query thecentral labto confirm that the result was transmitted correctly, since discrepancies may stem from data processing or timing issues. Once confirmed, a secondary query may be issued to the site to ensure CRF completion and alignment.

Conversely, if the CRF contains data but the central lab is missing results (option B), the issue is site-level, not vendor-level.

According to theGCDMP (Chapter: External Data Transfers and Reconciliation),priority for querying depends on the authoritative source— for lab data, thecentral labis considered the source of record.

Therefore,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Reconciliation, Section 6.1 – Reconciliation of Central Lab and CRF Data

ICH E6(R2) GCP, Section 5.5.3 – Source Data Verification and Vendor Reconciliation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Data Reconciliation and Traceability

Standard Operating Procedures (SOPs)are formal, controlled documents that definestandardized processesto ensure clinical trials are conducted in compliance withGood Clinical Practice (GCP), the study protocol, and regulatory requirements (such as ICH and FDA).

According toGood Clinical Data Management Practices (GCDMP)andICH E6(R2) GCP, SOPs are fundamental to quality management systems. They describehowtasks are performed, ensuring consistency, accountability, and traceability across all studies and team members. Proper adherence to SOPs guarantees that data areaccurately generated, documented, and reportedin compliance with ethical and regulatory standards.

Other options serve different purposes:

SAP (B)defines statistical methodology, not compliance control.

DMP (C)focuses on study-specific data handling, not organizational compliance.

CRFs (D)are tools for data collection but do not enforce compliance by themselves.

Therefore,option A (SOP)is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Quality Management and Compliance, Section 5.1 – Role of SOPs in Regulatory Compliance

ICH E6(R2) GCP, Section 2.13 and 5.1.1 – Quality Management Systems and SOP Requirements

FDA 21 CFR Part 312.50 – Sponsor Responsibilities and Compliance Systems

In Structured Query Language (SQL), theWHEREclause is used to filter records based on specified criteria. The query retrievesall columnsfrom thepatienttable (SELECT *) but onlythose rowswhere themedical_record_numbervalue is greater than 9000.

This means:

Thenumber of columns (fields)remains the same as the original table.

Thenumber of rows (records)will beequal to or less thanthe number of rows in thepatienttable, depending on how many patients meet the filter condition.

Hence, the result set can only beshorter or equal in lengthcompared to the original table. It cannot be longer, wider, or narrower, since no new rows or columns are created.

Therefore,option B—“Shorter or of equal length than the patient table”— is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.2 – Relational Database Queries and Filtering Logic

ICH E6(R2) GCP, Section 5.5.3 – Data Retrieval, Filtering, and Storage Principles

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Query Logic and Record Subsetting

When designing or configuringdata entry screenswithin an Electronic Data Capture (EDC) system, three critical components are required for each field:

Data Type– Defines the nature of the data (e.g., text, numeric, date).

Prompt– The label or question displayed to the user.

Response Format– Specifies how the user enters or selects data (e.g., free text, drop-down, checkbox).

According to theGCDMP (Chapter: EDC Systems and Database Design), these three attributes form thelogical data structurerequired to build and validate data entry interfaces. They ensure consistency in how information is captured, displayed, and validated during data entry.

Whileuser roles (A)andhelp text (D)are system-level configurations, not field-level specifications,page numbers (C)relate to printed CRFs rather than digital data screens.

Therefore,option B (Data type, prompt, and response format)correctly identifies the essential information needed to define data entry screens.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: EDC Systems and Database Design, Section 4.3 – Screen Design Specifications

CDISC CDASH Implementation Guide, Section 3.2 – Data Field Attributes

ICH E6(R2) GCP, Section 5.5.3 – Data Capture and Input Standards

For a well-maintained 100-subject study withongoing data cleaningandcompleted reconciliations, thedatabase lockprocess typically takesa few daysafter theLast Patient Last Visit (LPLV).

According to theGCDMP (Chapter: Database Lock and Archiving), the duration of the lock process depends on the level of data cleanliness at LPLV. If the study team has conducted continuous data cleaning, query resolution, and external data reconciliation throughout the trial, then the final lock steps (e.g., final data review, documentation, and approvals) can be completed in2–5 days.

However, if significant cleaning or reconciliation remains outstanding, lock may take several weeks. Since the question states that data are “maintained almost all clean,”Option B – a few days– is the appropriate estimate.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Lock and Archiving, Section 6.2 – Database Lock Preparation and Timelines

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Quality and Lock Procedures

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Lock and Archiving Procedures

In this scenario, the variable of interest—range of motion (ROM)—is aclinically measured, observer-dependent variable. The accuracy and reliability of such data depend primarily on theprecision and consistency of the measurement technique, not merely on data entry validation. Therefore, the most appropriatequality control (QC) methodisindependent verification of the measurement by a second qualified assessor during the visit(Option D).

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Quality Assurance and Control), quality control procedures must be tailored to the nature of the data. Forclinically assessed variables, especially those involving human judgment (e.g., physical measurements, imaging assessments, or subjective scoring),real-time verification by an independent qualified assessorensures that data are valid and reproducible at the point of collection. This approach directly addressesmeasurement bias,observer variability, andinstrument misuse, which are primary sources of data error in clinical outcome assessments.

Other options, while valuable, address onlydata consistency or plausibilityafter collection:

Option A (comparison to previous visit)andOption C (reviewing data listings)are retrospective data reviews, suitable for identifying trends but not preventing measurement error.

Option B (programmed edit checks)detects only extreme or impossible values, not measurement inaccuracies due to technique or observer inconsistency.

The GCDMP andICH E6 (R2) Good Clinical Practiceguidelines emphasize that data quality assurance should beginat the source, through standardized procedures, instrument calibration, and dual assessments for observer-dependent measures. Having anindependent second assessorensures inter-rater reliability and provides direct confirmation that the recorded value reflects an accurate and valid measurement.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 7.4 – Measurement Quality and Verification

ICH E6 (R2) Good Clinical Practice, Section 2.13 – Quality Systems and Data Integrity

FDA Guidance for Industry: Patient-Reported Outcome Measures and Clinical Outcome Assessment Data, Section 5.3 – Quality Control of Clinician-Assessed Data

SCDM GCDMP Chapter: Source Data Verification and Quality Oversight Procedures

In initial vendor discussions forexternal data integration(e.g., central lab, ECG, imaging vendors), themost critical and foundational topicis defining theacceptable record, field, and file formats.

According to theGCDMP (Chapter: External Data Transfers and Integration), establishing theData Transfer Specifications (DTS)early in the process ensures consistent structure, proper mapping, and compatibility between the vendor’s system and the sponsor’s database. These specifications define:

Data structure (variable names, formats, delimiters)

File naming conventions

Frequency of transfers

Methods of secure data transmission

Discussing formats first allows later alignment on data validation, quality metrics, and dictionary standards (which occur in subsequent stages). Without format agreement, all downstream processes risk misalignment, resulting in data incompatibility and rework.

Thus,option C (Acceptable record, field, and file formats)correctly represents the foundational focus of initial vendor discussions for ensuring data quality and integration efficiency.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Integration, Section 4.1 – Data Transfer Planning and Specification Development

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

FDA Guidance: Computerized Systems Used in Clinical Investigations, Section 6.3 – Data Import and Format Control

Theprimary benefitof using astandard medical dictionary(such asWHO Drug Dictionary,WHO-DD Enhanced, orRxNorm) in clinical data management is tostandardize the recording and representation of medicationstaken by study participants across all sites, countries, and data sources (Option A).

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Medical Coding and Dictionaries), standardized coding ensures that all variations of drug names — includingbrand names,generic names,abbreviations, andmisspellings— are consistently mapped to auniform dictionary term. This harmonization allows for accurate aggregation, analysis, and regulatory reporting of concomitant medications and investigational products across multiple studies and global sites.

For example, "Paracetamol" and "Acetaminophen" are the same compound but are known by different names in different regions. Coding both to the samepreferred term (PT)in the WHO Drug Dictionary ensures that all references are analyzed consistently in safety summaries and pharmacovigilance reports.

While other options describe secondary benefits:

Option B:Facilitating drug interaction analysis is an important downstream benefit, but it depends on having standardized coding first.

Option C:Identifying differences in medication components by country is a feature of dictionary metadata but not the primary goal.

Option D:Safety monitoring relies on consistent adverse event and drug data but is an overarching objective, not the direct function of dictionary coding.

Thus, theprimary benefitlies in ensuringconsistency, clarity, and interoperabilityof medication data across all clinical sites and systems, forming the foundation for reliable safety and efficacy analysis.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Medical Coding and Dictionaries, Section 6.1 – Purpose and Principles of Coding

WHO Drug Dictionary (WHO-DD) User Manual, Section 2.3 – Standardization of Medicinal Product Terminology

ICH E2B (R3) Clinical Safety Data Management – Data Elements for Transmission of Individual Case Safety Reports

FDA Study Data Technical Conformance Guide, Section 3.2 – Use of Controlled Terminology in Drug and Event Coding

Once a clinical database islocked, it becomesread-only— no further data modifications can be made by any users, including site personnel. This ensures that the data arefinalized, consistent, and auditablefor statistical analysis and regulatory submission.

According to theGCDMP (Chapter: Database Lock and Archiving), the lock process involves freezing the database to prevent accidental or unauthorized changes. After lock, access permissions are restricted, and all edit and update functions are disabled. If any corrections are required post-lock, the database must beunlocked under controlled procedures(with full audit trail documentation).

Thus,option C—The site study coordinator is not able to make the change— correctly reflects standard EDC functionality and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.2 – Database Lock Procedures and Controls

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity and Audit Trail Requirements

FDA 21 CFR Part 11 – Controls for Electronic Records and System Lock Functions

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

The optimal method for collectingfrequent patient-reported pain datais throughelectronic Patient-Reported Outcomes (ePRO)with built-inreminder functionality.

According to theGCDMP (Chapter: Electronic Data Capture Systems), ePRO systems provide avalidated, real-time, and user-friendly interfacefor subjects to record time-sensitive data accurately. The use ofautomated remindersensures compliance with protocol-specified data collection times, improving data completeness and accuracy.

Paper diaries (option A) are prone torecall bias and backfilling, while daily site calls (option B) areresource-intensiveand introduce human error. IVRS systems (option C) are acceptable but less efficient and user-friendly than modern ePRO applications, which can integrate timestamp validation, compliance monitoring, and real-time alerts.

ePRO systems also comply withFDA 21 CFR Part 11andICH E6 (R2)for audit trails, authentication, and validation, making them the preferred solution for repeated PRO data collection.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 6.1 – Use of ePRO for Repeated Measures

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 5 – ePRO Compliance and Validation

ICH E6 (R2) GCP, Section 5.5.3 – Electronic Data Systems and Recordkeeping

The most critical consideration is thatdata cleaning is an iterative process, and completing all necessary steps — includingquery generation, site resolution, and second-pass validation— cannot realistically be accomplished within two weeks after study close.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), data cleaning must occurcontinuously throughout the study, not only at the end. Post-database lock activities typically include running final validation checks, resolving outstanding queries, performing reconciliation (e.g., SAEs, labs, coding), and conducting final quality review.

Even in small studies,query turnaround and response cyclesfrom sites take time — typically2–4 weeks per iteration— making a two-week total cleaning period unrealistic.

Therefore,Option Dis correct: it would take more than two weeks to handle second-round (follow-up) queries and confirm final resolutions prior to database lock.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.4 – Ongoing vs. End-of-Study Data Cleaning

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Quality and Timeliness

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Management and Cleaning

When an audit identifiesmissing or undocumented training, the most appropriate and compliant response is toidentify the root causeof the issue andimplement corrective and preventive actions (CAPA)to ensure that similar findings do not recur.

According toGood Clinical Data Management Practices (GCDMP, Chapter: Quality Management and Auditing), effective quality systems require root cause analysis (RCA) for all audit findings. The process involves:

Investigating why the documentation gap occurred (e.g., poor tracking, outdated SOP, or lack of oversight).

Correcting the immediate issue (e.g., ensuring the developers complete or document training).

Updating processes, training systems, or oversight mechanisms to prevent recurrence.

While sending the two developers to training (D) addresses thesymptom, it does not resolve thesystemic issueidentified by the audit. Options B and C are non-compliant and do not address quality system improvement.

Therefore,option A (Identify the root cause and improve the process)is the best and CCDM-compliant response.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Quality Management and Auditing, Section 6.2 – Corrective and Preventive Actions (CAPA)

ICH E6(R2) GCP, Section 5.1.1 – Quality Management and Continuous Process Improvement

FDA 21 CFR Part 820.100 – Corrective and Preventive Action (CAPA) Requirements

In the context of aninvestigator-initiated trial (IIT)whereStandard Operating Procedures (SOPs)are not available, the most appropriate and compliant approach is todevelop a Data Management Plan (DMP) templateand then create astudy-specific DMPbased on that template (Option C).

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Management Planning and Study Start-up), theDMPis the central document that defines all processes, responsibilities, systems, and quality controls related to data collection, processing, validation, and database management throughout the clinical study. The DMP serves as a formal framework for ensuringdata integrity,traceability, andregulatory compliance, especially in the absence of established institutional SOPs.

While SOPs provide organizational-level standards, the DMP providesstudy-specific operational detail. In an investigator-initiated setting, researchers often lack institutional data management infrastructure, so the DMP must substitute for SOP guidance by detailing:

Data entry and validation procedures

Query management and resolution processes

CRF design and data flow specifications

Database design, backup, and security

Responsibilities of study personnel (investigator, data manager, statistician)

Quality control and audit trail practices

OptionA(“Data handling should be documented in a DMP”) is correct in principle but incomplete—without aDMP template, there is no standardized format or consistency across studies.

OptionB(developing full SOPs) is not practical for a single IIT; SOPs are organizational-level documents requiring longer development and approval cycles.

OptionD(briefly describing data management in the protocol) is insufficient, as the protocol should reference data management activities but not serve as the operational manual for them.

Therefore,Option Cprovides the most comprehensive, regulatory-compliant, and practical solution—ensuring structured documentation of all data management activities while maintaining flexibility for investigator-led research.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 5.2 – Data Management Plan (DMP) Development and Maintenance

ICH E6 (R2) Good Clinical Practice, Section 5.1 – Quality Management and Documentation Requirements

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 4 – Data Management and Documentation Practices

SCDM GCDMP, Chapter: Project Management in Data Management – Study-Specific Documentation and Planning in Investigator-Initiated Trials

Electronic submission standards, as established byFDA, CDISC, and ICH, require that an individual subject’scomplete Case Report Form (CRF)be submitted as aPortable Document Format (.pdf)file. ThePDF formatis universally recognized and accepted because it ensures that the structure, format, and visual fidelity of the CRF are preserved exactly as originally designed, regardless of software or hardware environment.

According to theFDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format (2006)andCDISC SDTM standards, sponsors must include asubject-level CRFin PDF form for each participant in the submission dataset. This requirement ensures that reviewers can trace data points from analysis datasets back to their source entries in the CRF, fulfilling the principles ofdata traceability and transparency.

TheGood Clinical Data Management Practices (GCDMP)also support this requirement, emphasizing that CRF archiving should maintain readability and regulatory accessibility. Formats like RTF, DOCX, or SAS datasets are not acceptable substitutes for regulatory CRF submission because they may alter formatting, structure, or introduce modifiable content, violating FDA data integrity principles.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Archiving and Submission

FDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format, April 2006

CDISC SDTM Implementation Guide, Section 5.3 – CRF Representation and Traceability

Maintainingtraceabilityof external data imports (such as laboratory results) is a fundamental principle of clinical data management. According to theGCDMP (Chapter: External Data Transfers and Integration), Data Managers mustretain an unaltered copy of the raw data exactly as received from the vendor.

This archived version serves as a reference for:

Data provenance verification,

Audit trail review, and

Discrepancy resolution between vendor and study database.

Since the central lab maintains its own audit trail, the Data Manager’s responsibility is to preserve theoriginal data transmission filebefore applying transformations, merges, or validations.

Options A, C, and D describe procedural safeguards but do not meet the regulatory requirement oftraceable data lineage. Onlyoption B (Maintaining a copy of the data as received)ensures compliance withICH E6(R2)andFDA 21 CFR Part 11standards for data traceability and integrity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Integration, Section 5.2 – Data Traceability and Version Control

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity and Source Data Verification

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Source Data Traceability and Archiving