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[General] Free PDF 2026 SCDM The Best CCDM: Test Certified Clinical Data Manager Collectio

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【General】 Free PDF 2026 SCDM The Best CCDM: Test Certified Clinical Data Manager Collectio

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SCDM CCDM Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 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 (Q136-Q141):NEW QUESTION # 136
Electronic submission standards require that an individual subject's complete CRF should be provided as what type of file:
  • A. Portable Document Format (.pdf)
  • B. Rich Text Format (.rtf)
  • C. Statistical Analysis System (.sas)
  • D. Microsoft Word (.docx)
Answer: A
Explanation:
Electronic submission standards, as established by FDA, CDISC, and ICH, require that an individual subject's complete Case Report Form (CRF) be submitted as a Portable Document Format (.pdf) file. The PDF format is 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 the FDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format (2006) and CDISC SDTM standards, sponsors must include a subject-level CRF in 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 of data traceability and transparency.
The Good 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

NEW QUESTION # 137
QA is conducting an audit on a study for ophthalmology which is ready for lock. Inconsistencies are found between the database and the source. Of the identified fields containing potential data errors, which fields are considered critical for this particular study?
  • A. Concomitant Medications
  • B. Medical History
  • C. Subject Identifier
  • D. Weight
Answer: A
Explanation:
In an ophthalmology clinical study, data criticality is determined by how directly a data element affects safety evaluation, efficacy assessment, and regulatory decision-making. According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), critical data fields are those that:
Have a direct impact on the primary and secondary endpoints, or
Are essential for safety interpretation and adverse event causality assessment.
Among the listed options, Concomitant Medications (Option B) are considered critical data for ophthalmology studies. This is because many ocular treatments and investigational products can interact with systemic or topical medications, potentially affecting ocular response, intraocular pressure, corneal healing, or visual function outcomes. Any inconsistency in concomitant medication data could directly influence safety conclusions or efficacy interpretations.
Other options, while important, are less critical for this study type:
Subject Identifier (A) is essential for data traceability and audit purposes but is not directly related to safety or efficacy outcomes.
Weight (C) may be relevant in dose-dependent drug trials but is rarely a pivotal variable in ophthalmology, where local administration (eye drops, intraocular injections) is common.
Medical History (D) provides contextual background but does not have the same immediate impact on endpoint analysis as current concomitant treatments that can confound the therapeutic effect or cause ocular adverse events.
Per GCDMP and ICH E6 (R2) GCP guidelines, data validation plans must define critical data fields during study setup, reflecting therapeutic area-specific priorities. For ophthalmology, concomitant medications, ocular assessments (visual acuity, intraocular pressure, retinal thickness, etc.), and adverse events are typically designated as critical fields requiring heightened validation, source verification, and reconciliation accuracy before database lock.
Thus, when QA identifies discrepancies between the CRF and source, the Concomitant Medications field (Option B) is the most critical to address immediately to ensure clinical and regulatory data integrity.
Reference (CCDM-Verified Sources):
Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.4 - Critical Data Fields and Data Validation Prioritization ICH E6 (R2) Good Clinical Practice, Section 5.18 - Monitoring and Source Data Verification FDA Guidance for Industry: Oversight of Clinical Investigations - A Risk-Based Approach to Monitoring, Section 5.3 - Identification of Critical Data and Processes SCDM GCDMP Chapter: Data Quality Assurance and Control - Therapeutic Area-Specific Data Criticality Examples (Ophthalmology Studies)

NEW QUESTION # 138
A study uses and collects pacemaker interrogation data for each patient weekly by selecting and downloading the data from the manufacturer's website. There are 200 patients in the study and it takes the Data Manager 30 minutes per file to download, import, and process the dat a. Assuming that the distribution of work is uniform over the six-month trial, how many Data Managers are needed for the activity data alone?
  • A. Two Data Managers per month
  • B. One Data Manager per month
  • C. Fifty percent of a Data Manager per month
  • D. Two and a half Data Managers per month
Answer: C
Explanation:
Let's calculate the workload:
200 patients × 30 minutes = 6,000 minutes/week
6,000 minutes ÷ 60 = 100 hours/week
Over 6 months (~26 weeks): 100 × 26 = 2,600 hours total
Assuming a full-time Data Manager works approximately 160 hours/month, over 6 months (960 hours) per full-time equivalent (FTE):
2,600 ÷ 960 ≈ 2.7 FTEs total for the entire study period
To find the average per month, we divide evenly over 6 months:
2.7 ÷ 6 ≈ 0.45 FTE per month, or approximately 50% of a Data Manager per month.
Thus, the correct answer is B. Fifty percent of a Data Manager per month.
This estimate follows GCDMP best practices in resource planning, ensuring adequate data management capacity for ongoing external data handling activities.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Project Management, Section 5.3 - Resource Estimation and Workload Planning ICH E6(R2) GCP, Section 5.1.1 - Quality Systems and Adequate Staffing

NEW QUESTION # 139
In an EDC study, an example of an edit check that would be inefficient to run at data entry is a check:
  • A. Across visits for consistency.
  • B. On the format of a date.
  • C. Against a valid numeric range.
  • D. Against a valid list of values.
Answer: A
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 # 140
Which type of edit check would be implemented to check the correctness of data present in a text box?
  • A. Front-end check
  • B. Manual Check
  • C. Programmed check
  • D. Back-end check
Answer: A
Explanation:
A front-end check is a type of real-time validation performed at the point of data entry-typically within an Electronic Data Capture (EDC) system or data entry interface-designed to ensure that the data entered in a text box (or any input field) is valid, logically correct, and within expected parameters before the user can proceed or save the record.
According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), edit checks are essential components of data validation that ensure data accuracy, consistency, and completeness. Front-end checks are implemented within the data collection interface and are triggered immediately when data are entered. They prevent invalid entries (such as letters in numeric fields, out-of-range values, or improper date formats) from being accepted by the system.
Examples of front-end checks include:
Ensuring a numeric field accepts only numbers (e.g., weight cannot include text characters).
Validating that a date is within an allowable range (e.g., not before the subject's date of birth).
Requiring mandatory fields to be completed before moving forward.
This differs from back-end checks or programmed checks, which are typically run later in batch processes to identify data inconsistencies after entry. Manual checks are human-performed reviews, often for context or data that cannot be validated automatically (e.g., narrative assessments).
Front-end edit checks are preferred wherever possible because they prevent errors at the source, reducing the number of downstream data queries and cleaning cycles. They contribute significantly to data quality assurance, regulatory compliance, and efficiency in data management operations.
Reference (CCDM-Verified Sources):
Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.2 - Edit Checks and Real-Time Data Validation FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 - Data Entry and Verification Controls ICH E6 (R2) Good Clinical Practice, Section 5.5 - Data Handling and Record Integrity CDISC Operational Data Model (ODM) Specification - Edit Check Implementation Standards

NEW QUESTION # 141
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