ISACA · DataSci-Fund
Validates foundational knowledge of data science, covering data management, the data science process, and data science concepts including data analysis, visualization, management systems, and the ability to extract meaningful insights for informed business decisions.
Practice Questions
591
≈ 3 practice exams
Duration
120 minutes
Passing Score
65%
Difficulty
FoundationalLast Updated
Feb 2026
Use this DataSci-Fund practice exam to prepare for Data Science Fundamentals Certificate with realistic questions, detailed explanations, and focused study modes. The practice bank includes 591 questions for ISACA DataSci-Fund, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The ISACA Data Science Fundamentals Certificate is an entry-level credential offered as part of ISACA's IT Certified Associate (ITCA) framework, which comprises five foundational badges covering computing, networking, cybersecurity, software development, and data science. This certificate validates a candidate's foundational knowledge and applied skills in data science, including data analysis, data visualization, data management systems, and the ability to extract meaningful insights to support informed business decisions. The exam blends traditional multiple-choice knowledge questions with performance-based questions delivered inside a live virtual lab environment, making it a practical, hands-on assessment rather than a purely theoretical one.
The credential covers three core domains: Data Management (42%), Data Science Process (33%), and Data Science Concepts (25%). Candidates demonstrate competency across data characteristics, data types, data structures, common statistical methods, key performance indicators, and data governance practices. As an ISACA-backed certificate, it carries the weight of a globally recognized professional organization known for IT governance, audit, and assurance credentials.
This certificate is designed for students, recent graduates, and early-career IT professionals who are looking to establish or formalize foundational knowledge in data science. It is particularly well-suited for individuals with up to one or two years of IT experience who want to validate their skills and differentiate themselves in the job market. Professionals seeking to transition into data-focused roles such as data analyst, junior data scientist, business intelligence analyst, or IT associate will find this credential a strong starting point.
Teams and organizations looking to upskill staff in data literacy and data-driven decision-making also benefit from this certificate. Because there are no prerequisites, it is accessible to career changers and those new to IT who want a structured, vendor-neutral introduction to data science concepts and processes.
ISACA requires no formal prerequisites to register for the Data Science Fundamentals Certificate exam. Candidates can register at any time without needing to demonstrate prior certifications, degrees, or professional experience. This open-access approach makes the certificate truly entry-level and accessible to anyone beginning their data science journey.
That said, candidates benefit most from a basic familiarity with computing concepts and general IT fundamentals before attempting the exam. Prior exposure to spreadsheet tools, basic statistics, or introductory programming concepts — while not required — will help candidates engage more effectively with the performance-based lab questions and the Data Science Process domain content.
The Data Science Fundamentals exam consists of 60 questions and must be completed within 120 minutes. It is delivered as a computer-based, remotely proctored exam, meaning candidates take it online under live supervision without visiting a physical testing center. The exam blends two question formats: traditional multiple-choice knowledge questions and performance-based questions administered within a live virtual lab environment, where candidates must demonstrate hands-on skills rather than simply recall facts.
Candidates must earn a score of 65% or higher to pass. Exam registration is continuous with no fixed deadlines, and eligibility is valid for 12 months from the date of registration. Testing appointments can be scheduled as early as 48 hours after payment. The exam fee is $120 USD for ISACA members and $144 USD for non-members. Candidates may reschedule without penalty up to 48 hours before their scheduled appointment.
Earning the Data Science Fundamentals Certificate signals to employers that a candidate has verified, vendor-neutral foundational knowledge in one of the most in-demand areas of technology. As part of ISACA's broader ITCA framework, the credential is recognized across approximately 130 IT occupations and is associated with over 210 specialized skills tracked in the labor market. For entry-level professionals, it provides a competitive differentiator in job postings where data literacy is increasingly expected even in non-specialist roles.
ISACA certifications broadly are associated with significant salary premiums — ISACA's own research and Foote Partners' IT Skills and Certifications Pay Index have consistently ranked ISACA credentials among the highest-paying in the industry. While the Data Science Fundamentals Certificate targets foundational roles such as data analyst, business intelligence associate, or IT generalist, it also serves as a stepping stone toward more advanced data science and governance certifications. Combined with ISACA's global reputation, this credential provides both immediate career differentiation and a structured pathway for long-term professional development in data-driven roles.
5 sample questions with answers and explanations. The full bank has 591 questions, enough for 3 full-length practice exams.
Preview — answers shown1. A healthcare analytics team uses K-Nearest Neighbors to classify patient risk levels based on age, blood pressure, cholesterol, and glucose measurements. Age ranges from 20 to 80 years, blood pressure from 80 to 200 mmHg, cholesterol from 100 to 400 mg/dL, and glucose from 50 to 300 mg/dL. Without preprocessing, the model achieves only 60 percent accuracy. Which preprocessing step is MOST critical for KNN performance? (Select one!)
Explanation
K-Nearest Neighbors calculates distances between data points using metrics like Euclidean distance. Without scaling, features with larger value ranges (cholesterol 100-400, glucose 50-300) dominate distance calculations over features with smaller ranges (age 20-80), causing the algorithm to effectively ignore smaller-scale features. Feature scaling using StandardScaler (zero mean, unit variance) or MinMaxScaler (0-1 range) ensures all features contribute equally to distance calculations. This preprocessing is mandatory for KNN, not optional. The dataset has no categorical variables requiring one-hot encoding. Dimensionality reduction may help but is not as critical as scaling. The problem does not mention missing values requiring imputation.
2. A data warehouse team implements Slowly Changing Dimension Type 2 for customer address tracking. Customer 12345 changes address from New York to California. The existing row has address New York with effective date 2023-01-01 and end date 9999-12-31. How should Type 2 handle this change? (Select one!)
Explanation
Slowly Changing Dimension Type 2 maintains full historical records by inserting a new row for the changed attribute while closing the previous row by updating its end date to the change date. This approach preserves complete history, allowing reports to show customer address at any point in time. The existing New York row remains with end date set to the change date, and the new California row begins with that date. Updating in place is Type 1. Adding a column is Type 3. Deletion loses historical context.
3. A retail company analyzes customer purchase data and discovers that customers who buy product A also tend to buy product B, but not all customers who buy product B purchase product A. This relationship has been validated across multiple stores and time periods. What has been created through this analysis? (Select one!)
Explanation
Knowledge is correct because the analysis has transformed information into actionable insights showing validated patterns and relationships that can guide business decisions like product placement or bundling strategies. This goes beyond information by providing understanding of customer behavior patterns. Data would be raw transaction records. Information would be simple summaries like total purchases. Wisdom would involve applying this knowledge with business judgment to make strategic decisions about marketing or inventory.
4. A machine learning engineer uses K-Nearest Neighbors (KNN) to classify customer risk levels based on four features: annual income (range 20000-200000), age (range 18-85), account balance (range 0-50000), and number of transactions (range 1-500). The model performs poorly with accuracy of 58 percent. Which action would MOST likely improve KNN performance? (Select one!)
Explanation
Feature scaling is mandatory for KNN because the algorithm calculates distances between data points, and features with larger numeric ranges dominate distance calculations. Annual income (range 180000) contributes vastly more to distance than age (range 67), causing the model to effectively ignore age and transaction count. StandardScaler (zero mean, unit variance) or MinMaxScaler (scale to 0-1 range) equalizes feature influence. Increasing K to 50 is excessive and would lead to oversmoothing where the model becomes too simple. Manhattan distance may help in some scenarios but does not address the core problem of unscaled features with different ranges. Removing age based on numeric range is incorrect; age may have important predictive power and should not be removed without proper feature importance analysis.
5. A quality control process measures product dimensions with a mean of 50.2 mm and standard deviation of 0.8 mm across Product Line A, and a mean of 200.5 mm with standard deviation of 1.2 mm across Product Line B. Which statistical measure allows meaningful comparison of variability between these product lines? (Select one!)
Explanation
The coefficient of variation (CV = σ/μ × 100%) is a dimensionless measure that allows comparison of variability across different scales. For Product A: CV = 0.8/50.2 × 100% = 1.59%, and Product B: CV = 1.2/200.5 × 100% = 0.60%, revealing Product A has higher relative variability despite smaller absolute standard deviation. Direct standard deviation comparison is misleading when means differ substantially. Variance comparison suffers the same scale issue. Range comparison does not account for central tendency or distribution shape.
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