CALIFORNIA RESEARCH DATA SPECIALIST (RDS) EXAMINATION ACTUAL EXAM COMPLETE QUESTIONS AND DETAILED SOLUTIONS LATEST UPDATE THIS YEAR JUST RELEASED

CALIFORNIA RESEARCH DATA
SPECIALIST (RDS) EXAMINATION ACTUAL
EXAM COMPLETE QUESTIONS AND
DETAILED SOLUTIONS LATEST UPDATE
THIS YEAR JUST RELEASED



California Research Data Specialist (RDS) Examination —
Coverage
 Statistical analysis methods and interpretation of
quantitative findings
 Research design principles for observational and
experimental studies
 Data cleaning, transformation, validation, and
quality assurance procedures
 Database concepts, SQL querying, and relational data
structures
 Programming fundamentals using analytical tools
such as SAS, R, Python, or SQL
 Data visualization principles and dashboard/report
preparation techniques

, Survey methodology, sampling methods, and
population estimation strategies
 Public sector data reporting standards and
compliance requirements
 Regression analysis, correlation, and predictive
modeling applications
 Probability distributions, hypothesis testing, and
confidence intervals
 Data governance, confidentiality, and ethical
handling of sensitive information
 Research documentation, metadata management, and
reproducibility practices
 Project coordination, stakeholder communication,
and technical reporting
 Spreadsheet analysis, automation, and large dataset
management
 Interpretation of policy-oriented research findings for
decision-making purposes
 Record linkage, data integration, and cross-system
data reconciliation
 Descriptive and inferential statistical methodologies
 Time-series analysis and trend interpretation methods

,  Geographic and demographic data analysis concepts
 Error detection, outlier management, and data
integrity assessment
 Performance measurement, KPI development, and
operational analytics
 Statistical software troubleshooting and validation
procedures
 Data warehousing concepts and
extraction/transformation/loading processes
 Research presentation skills and visualization
interpretation techniques
 Quality control procedures for analytical and research
environments


1.
A research analyst notices duplicate client records
producing inflated service utilization counts within a
statewide database analysis project. Which corrective
action best preserves reporting accuracy before statistical
modeling begins?
A. Increase sample size to offset duplicated records
B. Remove or merge duplicate records using unique
identifiers

, C. Ignore duplicates because regression models adjust
automatically
D. Replace duplicated values using random sampling
procedures
Answer: B
Rationale: Duplicate records distort counts, rates, and
model outcomes. Deduplication using reliable identifiers
preserves dataset integrity before analysis begins.


2.
A program evaluator wants to determine whether a
training initiative significantly improved employee
productivity after implementation. Which statistical
procedure is generally most appropriate when comparing
average performance before and after training for the same
participants?
A. Paired sample t-test
B. Chi-square goodness-of-fit test
C. Frequency distribution analysis
D. Independent randomization procedure
Answer: A