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CCDM Übungsmaterialien & CCDM Lernführung: Certified Clinical Data Manager & CCDM Lernguide

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SCDM CCDM Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 2

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.

Thema 3

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.

Thema 4

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.

Thema 5

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.

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SCDM Certified Clinical Data Manager CCDM Prüfungsfragen mit Lösungen (Q139-Q144):

139. Frage
Which type of edit check would be implemented to check the correctness of data present in a text box?

A. Manual Check
B. Front-end check
C. Programmed check
D. Back-end check

Antwort: B

Begründung:
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

140. Frage
Which statement applies to the CRF Completion Guidelines (CCGs) for a multinational study?

A. CCGs must contain the list of acceptable abbreviations to be used in the CRF
B. CCGs must be translated and back-translated in each local language used in the study
C. CCGs can instruct sites to write in their local language as long as the CRA is fluent in this language
D. CCGs can instruct sites to use any abbreviations if they are documented in the subject source notes

Antwort: A

Begründung:
The Case Report Form (CRF) Completion Guidelines (CCGs) are critical documents that guide site staff on how to accurately and consistently record data on CRFs across all participating sites, especially in multinational trials.
According to the Good Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), one of the key components of the CCGs is a list of acceptable abbreviations and conventions to be used during CRF entry. This standardization ensures data consistency across languages and countries, reduces ambiguity during data review, and facilitates database design and coding accuracy.
While translation (A) may be useful for training materials, it is not required for CCGs unless specified by regulatory bodies. Options C and D are incorrect because data collection should adhere to standardized terms in English (or the study's official language) - allowing free use of local languages or arbitrary abbreviations introduces inconsistencies.
Hence, option B - "CCGs must contain the list of acceptable abbreviations to be used in the CRF" - is correct.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 5.3 - CRF Completion Guidelines and Standardization ICH E6(R2) GCP, Section 5.5.3 - Consistency and Data Recording Requirements FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 - Data Entry Conventions and Documentation

141. Frage
A study takes body-composition measurements at baseline using a DEXA scanner. Which information is needed to correctly associate the body-composition data to the rest of the study data?

A. Study number and subject number
B. Study number and visit number
C. Subject number and visit number
D. Subject number

Antwort: C

Begründung:
To properly associate body-composition data (from a DEXA scanner) with other study data, both the subject number and the visit number are required.
According to the GCDMP (Chapter: Data Management Planning and Study Start-up), every clinical data record must be uniquely identifiable and linkable to a specific subject and study event. The subject number identifies the participant, while the visit number defines the temporal context in which the measurement was taken.
Without both identifiers, data integration becomes ambiguous-especially if multiple assessments occur over time (e.g., baseline, week 12, end of study). Including both ensures data traceability, integrity, and alignment with the protocol-defined schedule of events.
Study number (option A) alone does not distinguish between visits or subjects, and visit number alone (option C) lacks linkage to the individual participant.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.4 - Data Linking and Identification Requirements ICH E6 (R2) GCP, Section 5.5.3 - Data Traceability Principles FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations - Data Identification Requirements

142. Frage
A Clinical Data Manager reads a protocol for a clinical trial to test the efficacy of an antiviral to counteract a new epidemic. The stated primary efficacy endpoint is 3-month survival. Which data element is needed for the primary efficacy endpoint?

A. Birth date
B. Date of autopsy
C. Death date
D. Cause of death

Antwort: C

Begründung:
When the primary efficacy endpoint in a clinical trial is 3-month survival, the key data element required is the death date. This is because the survival endpoint is determined by calculating whether the subject lived or died within a defined time frame from study enrollment or randomization.
According to the GCDMP (Chapter: Data Management Planning and Study Start-up), the Clinical Data Manager (CDM) must identify and ensure the capture of all critical data elements necessary to evaluate the study endpoints. For time-to-event analyses (e.g., survival studies), accurate event dates (death date) are essential for endpoint derivation and statistical analysis.
Other data elements such as cause of death or date of autopsy (options B and C) may support secondary analyses or safety reviews but are not necessary to determine the survival endpoint itself. Similarly, birth date (option D) contributes to demographic data but is unrelated to the primary efficacy outcome.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.4 - Critical Data Identification for Endpoints ICH E9 - Statistical Principles for Clinical Trials, Section 2.2.3 - Time-to-Event Data Considerations FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development

143. Frage
When reviewing local lab data from a paper study, a Data Manager notices there are lab values not entered. What should the Data Manager request data-entry personnel do?

A. Issue a query
B. Flag the module for review
C. Call the patient to verify the information
D. Nothing

Antwort: A

Begründung:
When laboratory data are missing from a paper-based clinical study, the Data Manager should direct data-entry personnel to issue a query to the investigative site for clarification or correction.
According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), every missing, inconsistent, or out-of-range data point must be reviewed and, if necessary, resolved through the formal query management process. This ensures that all discrepancies between the source documents and database entries are properly documented, traceable, and auditable.
Data-entry staff are not authorized to infer or fill in missing information. They must escalate such discrepancies to the site via query, preserving data integrity and regulatory compliance with ICH E6 (R2) and FDA 21 CFR Part 11. Calling the patient directly (option B) would violate confidentiality and site communication protocol, while simply flagging or ignoring the issue (options A and D) would not meet GCDMP query resolution standards.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.2 - Query Management and Resolution ICH E6 (R2) Good Clinical Practice, Section 5.18.4 - Communication of Data Discrepancies FDA 21 CFR Part 11 - Electronic Records; Query Audit Trails Requirements

144. Frage
......

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