Free Practice Questions for SCDM CCDM Exam

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

When importing external data (e.g., laboratory or imaging results) into a clinical database, thefirst stepin data import quality control is to verify thatincoming files conform to the pre-specified data transfer specifications (DTS).

According to theGCDMP (Chapter: External Data Transfers and Integration), theData Transfer Specificationdefines file structure, variable names, data types, delimiters, record counts, and validation rules. The initial import check confirms that the received file matches the technical and structural requirements before content or record consistency is evaluated.

Subsequent checks—such as record counts (A), data consistency with existing database (C), and internal logical consistency (D)—are performed only after the file structure is validated and confirmed to match the specifications. Failure to perform this first check may cause import errors or corrupted data loads.

Thus, thefirst and most critical verification stepis ensuringfile conformity to the agreed data transfer specifications, makingoption Bcorrect.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers, Section 4.2 – Data Transfer File Validation and Import Checks

ICH E6(R2) GCP, Section 5.5.3 – Validation of Computerized Systems and Data Imports

Blood pressure is aquantitative physiological measurement, typically consisting oftwo continuous numeric values: systolic and diastolic pressure. Therefore, the most appropriate and scientifically valid method of data collection is to usetwo continuous variables(e.g., systolic = 120 mmHg, diastolic = 80 mmHg).

According to theGCDMP (Chapter: CRF Design and Data Collection), data fields must be designed to capture the mostprecise, accurate, and analyzableform of clinical data. Numeric data should be collected using numeric data types to allow for range checks, calculations (e.g., mean arterial pressure), and statistical analysis.

Options such as categorical representations (radio buttons or check boxes) introduce rounding, data loss, and analytic limitations. Coding a verbatim diagnosis (option A) is inappropriate for numeric vital sign data and violates the principle of capturing data at the most granular level.

Thus, the correct and validated method per CCDM standards istwo continuous variables, ensuring accuracy, traceability, and analytical flexibility.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.2 – Best Practices for Quantitative Data Capture

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Accuracy and Collection Standards

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Data Format and Structure Requirements

According toICH E6(R2) Good Clinical Practice (GCP), Section 5.18.1, theSponsoris ultimatelyresponsible for developing and implementing the Monitoring Plan.

The Monitoring Plan defines:

Theextent and nature of monitoring(e.g., on-site, remote, risk-based).

Theresponsibilities of monitors.

Thecommunication and escalation proceduresfor data quality and protocol compliance.

While theCRO (B)orMonitor (D)may perform monitoring activities under delegation, theSponsorretains legal accountability for ensuring a compliant and effective plan is developed and maintained. TheData Manager (C)may contribute by outlining data review workflows, but is not responsible for authoring or owning the plan.

Therefore,option A (Sponsor)is the correct answer.

Reference (CCDM-Verified Sources):

ICH E6(R2) GCP, Section 5.18.1 – Purpose and Responsibilities for Monitoring

SCDM GCDMP, Chapter: Regulatory Compliance and Oversight, Section 5.3 – Sponsor Responsibilities in Monitoring and Quality Assurance

FDA Guidance for Industry: Oversight of Clinical Investigations – Sponsor Responsibilities (2013)

Inpaper-based trials, site staff (e.g., study coordinators) record data manually on paper Case Report Forms (CRFs), which are later transcribed by data entry personnel into an electronic database.

However, inEDC-based studies, thesite coordinatoris directly responsible forentering data into the EDC system. This eliminates the need for centralized double data entry and shortens data cleaning timelines.

TheGCDMP (Chapter: Electronic Data Capture Systems)states that EDC systems shift certain tasks, includingdata entry, initial query response, and source verification preparation, to the site level. Yet,data entryremains the most significant additional responsibility compared to paper-based studies.

Option A (Query resolution)is performed in both EDC and paper-based systems.

Option C (Data curation)is typically a Data Management function.

Option D (Medical record abstraction)is part of source documentation, not specific to EDC.

Thus,option B (Data entry)is correct — it is the additional site coordinator duty unique to EDC environments.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Electronic Data Capture (EDC) Systems, Section 5.3 – Site Responsibilities and Workflow Changes

ICH E6(R2) GCP, Section 5.5.3 – Data Entry and Role Delegation in Computerized Systems

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 – Site-Level Data Entry Controls

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

Thesite-work dimension of site performanceevaluates how efficiently sites manage and resolve data-related tasks — particularly query resolution, data entry, and correction timelines. Among the given metrics, themedian and range of time from query generation to resolution (D)directly measures the site’s responsiveness and data management efficiency.

According to theGCDMP (Chapter on Metrics and Performance Measurement), this indicator helps identify sites that delay query resolution, which can impact overall study timelines and data quality. Tracking this metric allows the data management team to proactively provide additional training or communication to underperforming sites.

Other options measure different aspects of project progress:

Areflects overall database closure speed.

BandCrelate to study startup and enrollment readiness, not ongoing data work.

Thus,option Daccurately represents asite performance timeliness metric, aligning with CCDM principles for operational performance measurement.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Metrics and Performance Management, Section 5.4 – Site Query Resolution Metrics

ICH E6(R2) Good Clinical Practice, Section 5.18 – Monitoring and Site Performance Oversight

TheScope of Work (SOW)is a project management document that defineswhat services are includedin the work agreement between the sponsor and the CRO or vendor. It outlines deliverables, responsibilities, timelines, and budget allocations.

According to theGCDMP (Chapter: Project Management in Data Management), the SOW includes specifications such as:

The number and frequency ofdata transfers,

Database build and lockmilestones,

Quality control deliverables, and

Resource allocationfor data management tasks.

The SOW does not cover operational site-level details such as monitoring schedules (B), protocol sampling details (C), or personnel contact lists (D).

Therefore,option A (number of data transfers budgeted for a project)correctly identifies a use case directly addressed in the SOW.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Project Management, Section 4.1 – Scope of Work and Resource Planning

ICH E6(R2) GCP, Section 5.5 – Sponsor Oversight and Data Management Responsibilities

PMI Project Management Framework – Scope Definition and Deliverable Specifications

In a relational clinical database, themost efficient and normalized structurefor data collected repeatedly over time—such asvital signs—isone record per patient per visit.

Each patient will have multiple records, one for each visit when vital signs are assessed. This structure supports:

Time-based analysis (e.g., trends across visits),

Accurate data linkage with visit-level metadata, and

Efficient querying for longitudinal data.

According to theGCDMP (Chapter: Database Design and Build), the relational design principle dictates that data should be stored at thelowest unique level of observation. Since vital signs vary by both patient and visit, the combination ofpatient ID + visit IDforms a unique key for each record.

Option A (per visit) lacks patient identification, while options B and D aggregate data too broadly, losing temporal detail.

Thus,option C (One record per patient per visit)correctly represents the normalized design structure.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 4.2 – Normalization and Table Structure

CDISC SDTM Implementation Guide, Section 5.3 – Visit-Level and Observation-Level Data Structures

ICH E6(R2) GCP, Section 5.5.3 – Data Handling Principles

Adding multiple new sites late in the enrollment period creates aconcentrated influx of new datanear the end of the study. These sites typically start enrolling patients later, resulting in a“bolus” of Case Report Forms (CRFs)that must be entered, validated, and cleaned within a shorter timeframe to meet database lock deadlines.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Project Management and Data Flow), late site activation compresses the timeline for data management tasks, necessitating increased resources fordata entry, query management, and cleaning. Data management teams must anticipate this surge and plan accordingly—either by increasing staffing or revising timelines to prevent bottlenecks and maintain quality.

Whileoption D(increased query rates) can occur, it is a secondary effect. Themost direct and consistent impactis the surge in data volume requiring expedited processing near study end.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Project Management, Section 5.3 – Managing Changes in Site Activation and Data Flow

ICH E6(R2) GCP, Section 5.1 – Quality Management and Oversight