ISACA · DataSci-Fund
Data Science Fundamentals Certificate Practice Test
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.
Exam Details
Questions
591
Duration
120 minutes
Passing Score
65%
Difficulty
FoundationalLast Updated
Feb 2026
Data Science Fundamentals Certificate Practice Exam Preparation
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.
Exam Domain Breakdown
Data Management42%
Data Science Process33%
Data Science Concepts25%
Exam Overview
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.
Who Should Take This Exam
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.
Prerequisites
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.
Exam Format
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.
Skills Measured
- 1.Data Management (42%): Covers foundational concepts and practices for handling data throughout its lifecycle, including data characteristics, data types, data structures, data governance principles, data storage systems, and data quality management. Candidates must demonstrate understanding of how data is organized, stored, and maintained within enterprise environments.
- 2.Data Science Process (33%): Focuses on the methodologies and workflows used in data science initiatives, including problem framing, data collection, data preparation, exploratory data analysis, model building, evaluation, and deployment. Candidates demonstrate knowledge of how data science projects are structured and executed from start to finish.
- 3.Data Science Concepts (25%): Addresses core terminology, theoretical principles, and foundational ideas underlying the data science discipline, including key definitions, common statistical methods, KPI measurement, data visualization techniques, and how data analysis supports business decision-making.
Study Tips
- Use the official ISACA Data Science Fundamentals online interactive course as your primary study resource — it is authored by global industry experts, aligned directly to the three exam domains, and awards 9.5 CPE credits upon completion.
- Purchase or access the official ISACA study guide, which outlines exam objectives, provides domain-by-domain coverage, and includes example questions with recommended answers to help calibrate your understanding of the expected depth.
- Spend dedicated practice time inside the virtual lab environment. Since the exam includes performance-based questions in a live lab, hands-on familiarity with executing data tasks is critical and cannot be replaced by reading alone. ISACA's lab package provides 12-month access.
- Prioritize the Data Management domain in your preparation, as it accounts for the largest share of exam content at 42%. Focus on data types, data structures, lifecycle management, and governance concepts before moving to the other two domains.
- Practice with ISACA's official sample questions to familiarize yourself with both the multiple-choice format and the style of performance-based prompts. Timed practice under realistic conditions will help you manage the 2-hour window effectively across 60 questions.
- Review foundational statistics and KPI concepts as part of your Data Science Concepts preparation, paying particular attention to how statistical methods are applied to interpret data sets and support business decisions.
- Join the ISACA community forums and study groups, where candidates share exam experiences and preparation strategies. ISACA membership ($45 for students) also reduces the exam fee and provides access to additional resources.
Career Benefits
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.
Sample Questions
5 sample questions with answers and explanations. Start a practice session to test yourself across all 591 questions.
Preview — answers shown1. A research team conducts a hypothesis test to determine if a new teaching method increases student test scores compared to the traditional method. They want to detect only improvements, not decreases. Which hypothesis test approach should they use? (Select one!)
AOne-tailed test with alpha level 0.10
BOne-tailed test with alpha level 0.05 in the right tail
CTwo-tailed test with alpha level 0.025
DTwo-tailed test with alpha level 0.05
Explanation
A one-tailed test with alpha in the right tail is correct because the research hypothesis has a specific direction (increases only). One-tailed tests allocate the entire alpha level to testing significance in one direction, providing greater statistical power for detecting the predicted effect. Two-tailed tests split alpha between both directions and are appropriate when effects could go either way. Using alpha 0.10 would be too liberal unless specifically justified by the research context.
2. A data quality team discovers that 8% of customer records have NULL values in the phone_number field, distributed randomly across all customer segments. The field is not required for critical business processes. Which approach is MOST appropriate? (Select one!)
AReplace NULL values with a default phone number like 000-000-0000
BDelete all rows with missing phone numbers to ensure data quality
CRetain NULL values and flag records for optional future collection
DImpute missing phone numbers using the most frequent phone number
Explanation
Retaining NULL values with optional flagging is most appropriate when missing data is not critical and randomly distributed. Deleting 8% of records loses valuable customer data unnecessarily. Imputing with the most frequent phone number creates fictional contact information that would cause operational problems. Replacing with default values creates false data that appears valid but is not. Since phone numbers are not critical and data is MCAR (Missing Completely At Random), retaining NULLs preserves data integrity.
3. A logistics company analyzes delivery times and finds the distribution has a coefficient of variation (CV) of 45 percent for air shipments and 28 percent for ground shipments. Both services have similar mean delivery times. The operations manager needs to select the more consistent service for time-sensitive contracts. Which delivery method should be recommended and why? (Select one!)
AAir shipments because they have higher coefficient of variation indicating better consistency
BGround shipments because they have lower coefficient of variation indicating less relative variability
CAir shipments because coefficient of variation measures central tendency
DBoth services are equally consistent since CV does not measure variability
Explanation
Coefficient of variation measures relative variability as a percentage of the mean, calculated as (standard deviation / mean) × 100 percent. Lower CV indicates more consistent performance relative to the mean. Ground shipments with 28 percent CV demonstrate less relative variability compared to air shipments at 45 percent CV, making ground shipments more predictable and consistent for time-sensitive contracts. CV is specifically designed to compare variability across datasets with different scales or means, making it ideal for this comparison.
4. A manufacturing company collects sensor data from production equipment and stores it in HDFS (Hadoop Distributed File System). The data exhibits high Volume, Velocity, Variety, and Veracity characteristics. Which Big Data V is PRIMARILY addressed by implementing data validation and cleansing workflows? (Select one!)
AVolume, by reducing the total amount of data through quality filtering
BVelocity, by increasing the speed of data processing through parallel validation
CVeracity, by improving data quality and trustworthiness through validation
DVariety, by standardizing diverse data formats into consistent schemas
Explanation
Veracity refers to data quality, accuracy, and trustworthiness. Implementing data validation and cleansing workflows directly addresses veracity by detecting and correcting errors, inconsistencies, and inaccuracies, making data more reliable for analysis. Volume refers to data quantity and scale, which validation does not primarily address. Velocity refers to speed of data generation and processing. While parallel validation may improve speed, the primary goal is quality, not velocity. Variety refers to different data types and formats. While standardization may occur during cleansing, the main purpose of validation workflows is ensuring quality and trustworthiness, which is the definition of veracity.
5. A data privacy officer implements protection techniques for a customer database containing payment card numbers. The production database encrypts card numbers using AES-256. The test environment replaces real card numbers with fictional but structurally valid numbers maintaining the same format. Archived data stores only cryptographic hashes of card numbers. Which techniques are being used? (Select one!)
AProduction uses masking, test uses hashing, archive uses tokenization
BProduction uses hashing, test uses masking, archive uses encryption
CProduction uses encryption, test uses tokenization, archive uses hashing
DProduction uses tokenization, test uses encryption, archive uses masking
Explanation
Encryption uses algorithms like AES-256 to protect data reversibly with keys, suitable for production systems needing to decrypt for legitimate transactions. Tokenization replaces sensitive data with fictional but format-preserving values that maintain data utility for testing without exposing real data, allowing applications to function with realistic but fake data. Hashing creates one-way cryptographic digests suitable for archives where retrieval is unnecessary but verification might be needed. Each technique serves distinct purposes: encryption for reversible protection, tokenization for format preservation, hashing for irreversible protection, and masking for partial obscuring. Understanding when to apply each technique is essential for comprehensive data protection strategies.
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