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100+ Free IBM Data Science Professional Certificate Practice Questions

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Converting a categorical variable like 'color' (red, green, blue) into binary indicator columns is called what?

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Key Facts: IBM Data Science Professional Certificate Exam

12 courses

Courses in the current program (including capstone)

Coursera

~4 months

Typical completion time at 10 hours/week

Coursera

70%+

Typical quiz passing threshold per course

Coursera grading

No expiry

Certificate and IBM badge validity

IBM

Beginner

No prior experience required

IBM / Coursera

Credly badge

IBM digital badge issued on completion

IBM Training

The IBM Data Science Professional Certificate is a Coursera multi-course program (no single proctored exam) assessed by graded quizzes, hands-on labs, and an applied capstone. Course quizzes typically require 70% or higher, and access is included with a Coursera subscription with financial aid available; the certificate has no expiry. It covers data science methodology and CRISP-DM, Python with NumPy and pandas, SQL, data visualization with Matplotlib, Seaborn, and Folium, and machine learning with scikit-learn (regression, classification, clustering, and evaluation metrics).

Sample IBM Data Science Professional Certificate Practice Questions

Try these sample questions to test your IBM Data Science Professional Certificate exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In John Rollins' Foundational Methodology for Data Science (taught in the IBM Data Science Methodology course), which stage comes first and frames the entire project?
A.Analytic approach
B.Business understanding
C.Data collection
D.Modeling
Explanation: Business Understanding is the first of the 10 stages. It clarifies the problem and goals from the sponsor's perspective so the rest of the project is aligned to a real need. Skipping it risks solving the wrong problem efficiently.
2A bank wants to predict whether a loan applicant will default ('yes' or 'no'). In the methodology's Analytic Approach stage, which technique class best fits this goal?
A.Regression model
B.Classification model
C.Clustering model
D.Association rules
Explanation: Predicting a categorical 'yes/no' outcome is a classification problem. The Analytic Approach stage maps the business question to a technique family, and binary outcomes call for classification models such as logistic regression or decision trees.
3Which CRISP-DM phase involves data cleaning, handling missing values, and feature engineering to build the modeling dataset?
A.Modeling
B.Evaluation
C.Data Understanding
D.Data Preparation
Explanation: Data Preparation covers all activities to construct the final dataset: cleaning, dealing with missing or invalid values, removing duplicates, formatting, integrating sources, and feature engineering. It typically consumes the largest share of project time.
4How many phases make up the CRISP-DM framework commonly referenced in the IBM data science methodology?
A.Six
B.Ten
C.Four
D.Five
Explanation: CRISP-DM (Cross-Industry Standard Process for Data Mining) has six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The process is iterative, with arrows looping back between phases.
5Which type of analytics answers the question 'What is likely to happen?' rather than 'What happened?'
A.Descriptive analytics
B.Diagnostic analytics
C.Predictive analytics
D.Prescriptive analytics
Explanation: Predictive analytics uses historical data and statistical or machine-learning models to forecast future outcomes ('what is likely to happen'). It sits between diagnostic ('why it happened') and prescriptive ('what should we do') analytics.
6In the methodology, what is the primary purpose of the Evaluation stage that occurs before deployment?
A.To engineer new features
B.To collect additional raw data
C.To assess whether the model meets quality and business success criteria
D.To define the analytic approach
Explanation: Evaluation determines whether the model adequately addresses the business problem and meets quality criteria, using metrics and diagnostic measures such as a confusion matrix or ROC curve. If it falls short, the process loops back to refine the model or data.
7The methodology emphasizes that data science is iterative. After deploying a model, which stage gathers real-world performance information to inform the next cycle?
A.Feedback
B.Data requirements
C.Analytic approach
D.Modeling
Explanation: Feedback is the final stage of the Foundational Methodology. After deployment, users and monitoring provide feedback on the model's real-world performance, which loops back to refine the model and keep it relevant over time.
8During Data Understanding, a data scientist checks for missing values, duplicates, and outliers. This activity is best described as assessing what?
A.Deployment strategy
B.Model accuracy
C.Data quality
D.Business objectives
Explanation: Verifying data quality - completeness, accuracy, consistency, and presence of missing values, duplicates, or outliers - is a core Data Understanding activity. Poor quality discovered here drives the cleaning work done in Data Preparation.
9What distinguishes supervised learning from unsupervised learning?
A.Supervised learning uses labeled target data; unsupervised learning does not
B.Supervised learning only works on images
C.Unsupervised learning always produces higher accuracy
D.Unsupervised learning requires a confusion matrix
Explanation: Supervised learning trains on data with known labels or target values (e.g., classification, regression), learning a mapping from features to the target. Unsupervised learning, such as clustering, finds structure in data with no labeled outcome.
10A telecom company wants to group customers into segments with similar behavior without any predefined labels. Which approach fits?
A.Decision tree classification
B.Linear regression
C.Logistic regression
D.Clustering
Explanation: Segmenting customers into similar groups with no predefined labels is an unsupervised clustering task, often solved with k-means or hierarchical clustering. The model discovers natural groupings from feature similarity.

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