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
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
Preview — answers shown5 sample questions with correct answers and explanations. Start a practice session to test yourself across all 591 questions.
1. A Python data analyst uses the following code: df.loc[df['age'] > 30, 'category'] = 'senior'. What operation does this statement perform? (Select one!)
AFilters rows where age is greater than 30 and returns the category column
BCreates a new DataFrame with only senior category members
CCalculates the correlation between age and category variables
DUpdates the category column to senior for rows where age exceeds 30
Explanation
The loc indexer with assignment updates values in place. The boolean condition df['age'] > 30 selects rows, and the assignment sets the category column to senior for those rows. This is a conditional update operation. Simply filtering without assignment would not change values. Creating a new DataFrame would require additional syntax. Correlation calculation uses different methods like corr(). The loc indexer allows both selection and assignment operations.
2. A predictive maintenance system for manufacturing equipment uses a Random Forest classifier to predict equipment failures. The data science team must select an appropriate number of trees (n_estimators) and determine how many features to consider at each split (max_features). The dataset has 25 input features. Which combination represents best practice recommendations for Random Forest hyperparameters? (Select one!)
An_estimators=50, max_features=1
Bn_estimators=10, max_features=25
Cn_estimators=500, max_features='log2'
Dn_estimators=200, max_features='sqrt'
Explanation
Best practice for Random Forests recommends n_estimators between 100-500 trees (200 is a solid choice) and max_features='sqrt' which considers the square root of total features at each split for classification tasks. Using max_features='sqrt' with 25 features means approximately 5 features are randomly selected at each split, providing good diversity between trees while maintaining predictive power. Setting n_estimators=10 is too few trees. Using all features (max_features=25) reduces diversity between trees and increases overfitting risk. Using only 1 feature per split creates overly simplistic trees. While max_features='log2' is acceptable, 'sqrt' is the standard default recommendation for classification.
3. A database administrator must choose between DELETE and TRUNCATE operations to remove records from a large audit log table in a production environment. The operation must be recoverable if errors occur. Which operation should the administrator use? (Select one!)
ADROP TABLE followed by CREATE TABLE for complete reset
BUPDATE to set a deleted flag instead of physical removal
CTRUNCATE because it is faster with minimal logging
DDELETE because it supports rollback and transaction recovery
Explanation
DELETE is the correct choice because it logs individual row deletions and supports rollback within a transaction, providing the required recoverability. DELETE operations can be rolled back if errors occur during execution. TRUNCATE performs minimal logging and typically cannot be rolled back, violating the recovery requirement. DROP TABLE removes the entire table structure, not just records, and requires rebuilding the table. Using a deleted flag changes the requirement from physical deletion to logical deletion and does not actually remove records.
4. A data governance council establishes four levels of documentation for the organization's data management framework. Level 1 provides high-level mandatory directives about data classification. Level 2 specifies exact encryption key lengths and access control requirements. Level 3 offers recommended best practices for data quality monitoring. Level 4 contains detailed step-by-step instructions for executing data backup procedures. How should these levels be classified in hierarchical order? (Select one!)
ALevel 1: Procedures, Level 2: Standards, Level 3: Policies, Level 4: Guidelines
BLevel 1: Standards, Level 2: Policies, Level 3: Procedures, Level 4: Guidelines
CLevel 1: Guidelines, Level 2: Policies, Level 3: Procedures, Level 4: Standards
DLevel 1: Policies, Level 2: Standards, Level 3: Guidelines, Level 4: Procedures
Explanation
In ISACA governance frameworks, the hierarchy follows this order: Policies are high-level, mandatory directives establishing what must be done. Standards specify exact, measurable requirements defining how much or what quality level. Guidelines provide recommended best practices that are optional. Procedures contain detailed step-by-step instructions for executing specific tasks. Level 1 describes mandatory high-level directives (Policies). Level 2 specifies exact requirements like key lengths (Standards). Level 3 offers recommended practices (Guidelines). Level 4 provides detailed instructions (Procedures). Understanding this hierarchy is fundamental to implementing effective data governance aligned with ISACA principles.
5. A social media analytics platform stores billions of user interaction events including user_id, post_id, interaction_type, and timestamp. Query patterns primarily involve retrieving all interactions for a specific user within a date range. The system requires horizontal scaling across multiple servers to handle massive data volumes and high write throughput. Which database type is MOST appropriate for this use case? (Select one!)
ARelational database with B-tree indexes on user_id and timestamp
BColumn-family NoSQL database partitioned by user_id
CDocument NoSQL database with embedded interaction arrays
DGraph database with user and post nodes connected by interaction edges
Explanation
Column-family NoSQL databases like Cassandra are optimal for time-series data with high write throughput and horizontal scalability requirements. Partitioning by user_id enables efficient retrieval of all interactions for a specific user, and the columnar storage model handles time-range queries effectively. These databases are specifically designed for massive scale across distributed servers. Relational databases struggle with horizontal scaling at this scale. Document databases with embedded arrays become inefficient as interaction counts grow and do not partition well for this query pattern. Graph databases excel at relationship traversal but are not optimized for high-volume time-series writes and range queries. Column-family databases provide the best combination of scalability, write performance, and query efficiency for this scenario.
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