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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
  • 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.
Topic 5
  • 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.

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SCDM Certified Clinical Data Manager Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which competency is necessary for EDC system use in a study using the medical record as the source?

Answer: A

Explanation:
In studies where the medical record serves as the source document, the Electronic Data Capture (EDC) system users (typically study coordinators or site personnel) must have appropriate training on how to access and log into the medical record system. This competency ensures that data abstracted from the electronic medical record (EMR) are complete, accurate, and verifiable in compliance with Good Clinical Practice (GCP) and Good Clinical Data Management Practices (GCDMP).
According to the GCDMP (Chapter: EDC Systems and Data Capture) and ICH E6(R2), all personnel involved in data entry and verification must be trained in both the EDC and the primary source systems (e.g., EMR). This ensures that the integrity of data flow-from source to EDC-is maintained, and that personnel understand system access controls, audit trails, and proper documentation of source verification.
While resolving discrepant data (C) and screening subjects (A) are part of study operations, the competency directly related to EDC system use in EMR-based studies is the ability to properly log into and navigate the medical records system to extract source data.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Electronic Data Capture (EDC), Section 5.1 - Source Data and System Access Requirements ICH E6(R2) Good Clinical Practice, Section 4.9 - Source Documents and Data Handling FDA Guidance: Use of Electronic Health Record Data in Clinical Investigations, Section 3 - Investigator Responsibilities


NEW QUESTION # 36
In an EDC study, an example of an edit check that would be inefficient to run at data entry is a check:

Answer: C

Explanation:
In Electronic Data Capture (EDC) systems, edit checks are categorized based on when and how they are executed - typically immediate (at data entry) or batch (post-entry). Checks that require data from multiple visits or forms are generally inefficient to run at data entry because they depend on information that may not yet exist in the system.
According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), cross-visit consistency checks - such as comparing baseline and follow-up blood pressure or verifying date order between screening and dosing - should be executed as batch or scheduled validations, not at the point of data entry. Running these complex checks in real time can slow system performance, increase query load unnecessarily, and confuse site users if related data are not yet entered.
Conversely, edit checks against valid ranges, formats, or predefined value lists (options A, C, and D) are simple, local validations ideally performed immediately at data entry to prevent basic errors.
Therefore, cross-visit consistency checks (Option B) are best executed later, making them inefficient for real-time data entry validation.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.4 - Real-Time vs. Batch Edit Checks FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations - Section on Edit Checks and Data Validation Logic CDISC SDTM Implementation Guide - Section on Temporal Data Consistency Validation


NEW QUESTION # 37
ACME Intervention Co. is testing a new carotid artery stent in patients with coronary artery disease, in hopes of proving superiority over the current standard of care. After a subject signs consent, the surgeon enrolls the patient and retrieves information on which stent to use, but the surgeon does not share this information with the subject. Yesterday, the surgeon was instructed to use the control stent. Today, the surgeon has completed two surgeries: the first one the surgeon was instructed to use the control stent; the second one the surgeon was instructed to use the test stent. In what type of trial is the surgeon participating?

Answer: A

Explanation:
This scenario describes a single-blind trial, in which only one party-typically the subject-is unaware of the treatment assignment, while the investigator or surgeon knows which intervention is being administered.
In this case, the surgeon receives instructions on which stent (test or control) to use, meaning they are aware of treatment allocation. However, the subject is blinded to which device is being implanted. This setup minimizes subject bias while maintaining procedural safety since the surgeon must know which product to use.
Double-blind (A): Neither subject nor investigator knows the treatment.
Open-label (B): Both subject and investigator know the treatment.
Cross-over (D): Each subject receives both treatments in different periods.
Thus, the correct answer is C. Single-blind, as only the participant remains blinded in this surgical device trial design.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Clinical Trial Phases and Protocols, Section 3.2 - Study Blinding and Randomization Concepts ICH E6(R2) GCP, Section 1.10 - Definition of Blinding/Masking FDA Guidance for Industry: Design Considerations for Pivotal Clinical Investigations for Medical Devices, Section 5.3 - Blinding in Device Studies


NEW QUESTION # 38
In the transfer of obligations for a double-blind, multi-center trial, a sponsor has maintained the task of creating the randomization schedule. Who at the sponsor company should create the randomization schedule?

Answer: A

Explanation:
In a double-blind clinical trial, the randomization schedule must be generated by an independent biostatistician not directly involved in study operations or data management to preserve study blinding and integrity.
According to ICH E9 and the GCDMP (Chapter: Regulatory Requirements and Compliance), randomization generation and blinding must be handled in a way that prevents bias or unintentional unblinding of study personnel. The sponsor's biostatistician not assigned to the project (Option C) is the appropriate person because they have the necessary statistical expertise but remain operationally independent from study execution.
A project biostatistician (Option D) or programmer (Option A) directly involved in data analysis could inadvertently compromise blinding. The CRO biostatistician (Option B) should not perform this function if the sponsor retains randomization responsibility.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Requirements and Compliance, Section 6.4 - Randomization and Blinding ICH E9 - Statistical Principles for Clinical Trials, Section 5.4 - Randomization Procedures and Blinding FDA Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics, Section 4.3 - Maintaining Blinding Integrity


NEW QUESTION # 39
A study is collecting ePRO assessments as well as activity-monitoring data from a wearable device. Which data should be collected from the ePRO and activity-monitoring devices to synchronize the device data with the visit data entered by the site?

Answer: C

Explanation:
To synchronize data from electronic patient-reported outcomes (ePRO) and wearable activity-monitoring devices with site-entered visit data, both the study subject identifier and date/time are essential.
According to the GCDMP (Chapter: Data Management Planning and Study Start-up), each dataset must contain key identifiers that allow for accurate data integration and temporal alignment. In studies involving multiple digital data sources, time-stamped subject identifiers are necessary to ensure that the device-generated data correspond to the correct subject and study visit.
The subject identifier ensures data traceability and linkage to the appropriate participant, while date/time allows synchronization of device data (e.g., activity or physiological measurements) with the corresponding site-reported visit or event. Geo-spatial data (options C and D) are typically not relevant to study endpoints and pose unnecessary privacy risks under HIPAA and GDPR guidelines.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Integration and eSource Data, Section 5.2 - Data Alignment and Synchronization Principles FDA Guidance for Industry: Use of Electronic Health Record Data in Clinical Investigations, Section 4.2 - Data Linking and Synchronization ICH E6 (R2) GCP, Section 5.5.3 - Data Traceability and Integrity


NEW QUESTION # 40
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