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SCDM CCDM Exam Syllabus Topics:

TopicDetails
Topic 1
  • Review Tasks: This section measures the skills of Data Managers and involves reviewing protocols, CRFs, data tables, listings, figures, and clinical study reports (CSRs) for consistency, accuracy, and alignment with data handling definitions and regulatory requirements.
Topic 2
  • Coordination and Project Management Tasks: This domain evaluates the skills of a Clinical Systems Analyst in coordinating data management workload, vendor selection, scheduling, cross-team communication, project timeline management, risk handling, metric tracking, and preparing for audits.
Topic 3
  • Testing Tasks: This section measures the skills of Data Managers and involves creating test plans, generating test data, executing validation and user acceptance testing, and documenting results to ensure systems and processes perform reliably and according to specifications.
Topic 4
  • Design Tasks: This section of the CCDM Exam measures skills of Data Managers and covers how to design and document data collection instruments, develop workflows and data flows, specify data elements, CRF forms, edit checks, reports, database structure, and define standards and procedures for traceability and auditability.
Topic 5
  • Data Processing Tasks: This section measures skills of Clinical Systems Analysts and focuses on handling, transforming, integrating, reconciling, coding, querying, updating, and archiving study data while maintaining quality, consistency, and proper privileges over the data lifecycle.

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SCDM Certified Clinical Data Manager Sample Questions (Q102-Q107):

NEW QUESTION # 102
In a study, data are key entered by one person after which a second person enters the data without knowledge of or seeing the values entered by the first. The second person is notified during entry if an entered value differs from first entry and the second person's decision is retained as the correct value. Which type of entry is being used?

Answer: C

Explanation:
The described process is Blind Verification, also known as double data entry with blind verification. In this method, two independent operators enter the same data. The second operator is blinded to the first entry to avoid bias. When discrepancies arise, the system flags them for review, and the second entry (or an adjudicated value) is retained as the correct one.
According to GCDMP (Chapter: Data Entry and Data Tracking), blind double data entry is used primarily in paper-based studies to minimize transcription errors and ensure data accuracy.
Single entry (D): Only one operator enters data.
Manual review (B): Involves post-entry checking, not during entry.
Third-party compare (C): Used for reconciling external data sources, not CRF data.
Hence, option A (Blind verification) is the correct and CCDM-defined process.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Data Entry and Data Tracking, Section 5.1 - Double Data Entry and Verification Methods ICH E6(R2) GCP, Section 5.5.3 - Data Entry and Verification Controls FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 - Data Accuracy and Verification


NEW QUESTION # 103
Which mode of data entry is most commonly used in EDC systems?

Answer: D

Explanation:
The most common mode of data entry in Electronic Data Capture (EDC) systems is single data entry.
According to the GCDMP (Chapter: Electronic Data Capture Systems), EDC systems have built-in edit checks, validation rules, and audit trails that ensure data accuracy and integrity at the point of entry. These real-time validation capabilities make double data entry (a legacy practice from paper studies) unnecessary.
EDC systems automatically verify data as they are entered by site staff, generating queries for inconsistencies or out-of-range values immediately. Blind verification (option B) and third-party comparisons (option D) are not standard data entry modes but may be used for specialized reconciliation or external data imports.
Thus, single data entry (Option C) is the industry standard approach, ensuring both efficiency and compliance with FDA 21 CFR Part 11 and ICH E6 (R2) data integrity requirements.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 5.4 - Data Entry and Verification Processes ICH E6 (R2) Good Clinical Practice, Section 5.5.3 - Computerized Systems and Data Validation FDA 21 CFR Part 11 - Electronic Records and Electronic Signatures: Validation and Data Entry Requirements


NEW QUESTION # 104
Which is the most important reason for why a data manager would review data before a monitor reviews it?

Answer: A

Explanation:
The primary reason data managers review data before a monitor's review is to identify and flag discrepancies or inconsistencies so that site monitors can focus their efforts more efficiently during on-site or remote source data verification (SDV).
According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), proactive data review by data management staff ensures data completeness and accuracy by identifying missing, inconsistent, or out-of-range values. This pre-review helps streamline the monitoring process, reduces the volume of open queries, and enhances data quality.
Option A is true but not the main reason for pre-monitor review. Option C highlights a capability rather than a rationale. Option D is partially correct, but the GCDMP emphasizes process purpose, not prescriptive order. Thus, option B correctly captures the practical and process-oriented reason for early data review-to ensure data are ready and accurate for the monitor's review phase.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.3 - Data Review Timing and Purpose ICH E6(R2) GCP, Section 5.18 - Monitoring and Data Verification Requirements


NEW QUESTION # 105
In development of CRF Completion Guidelines (CCGs), which is a minimum requirement?

Answer: C

Explanation:
Case Report Form Completion Guidelines (CCGs) are essential study documents that instruct site staff on how to complete each field of the CRF correctly. A minimum requirement for CCGs, according to Good Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), is that they must include version control.
Option A describes an important design consideration but not a minimum compliance requirement. Option B is inaccurate, as CCGs must be approved and implemented before data collection begins, not after. Option D includes an irrelevant stakeholder (Marketing).
Therefore, option C-"CCGs must include a version control on the updated document"-is correct and compliant with CCDM and GCP standards.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 4.3 - Development and Maintenance of CRF Completion Guidelines ICH E6(R2) GCP, Section 8.2.1 - Essential Documents and Version Control Requirements


NEW QUESTION # 106
A Data Manager is designing a CRF for a study for which the efficacy data are not covered by the current SDTM domains. Which search should the Data Manager do?

Answer: D

Explanation:
When existing SDTM (Study Data Tabulation Model) domains do not cover specific efficacy data, the best practice is to first search for relevant data element standards that may be available through CDISC CDASH (Clinical Data Acquisition Standards Harmonization) or other recognized industry standards.
Per GCDMP (Chapter: Standards and Data Integration), Data Managers must ensure that new CRF elements are consistent with standardized definitions, controlled terminology, and data models to support interoperability, future analysis, and regulatory submission.
If no existing standards exist, only then should the Data Manager collaborate with the study team to define new elements - but standard searches always come first.
Thus, option C is correct - search for relevant data element standards ensures alignment with CDISC best practices and regulatory expectations.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Standards and Data Integration, Section 5.1 - Use of CDISC Standards in CRF Design CDISC CDASH Implementation Guide, Section 4.1 - Standardization of Data Collection Fields FDA Study Data Technical Conformance Guide (SDTCG), Section 2.4 - Use of Standard and Custom Domains


NEW QUESTION # 107
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