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Exam Review

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 review concepts for the IBM Data Science Professional Certificate exam. 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.

About the IBM Data Science Professional Certificate Exam

The IBM Data Science Professional Certificate is a beginner-friendly, fully online program on Coursera that teaches the end-to-end data science workflow. Across roughly a dozen courses it covers what data science is, the data science methodology (the Foundational Methodology for Data Science and CRISP-DM), Python for data science with NumPy and pandas, databases and SQL, data analysis and visualization with Matplotlib, Seaborn, and Folium, and machine learning with scikit-learn. Learners build a portfolio through hands-on labs and an applied capstone project, and earn an IBM digital badge issued through Credly. There is no single proctored exam; the credential is earned by passing graded quizzes, labs, and the capstone in each course.

Exam sponsor: IBM (via Coursera). The requirements and fees below concern the certification or admission exam, separate from our free practice resources.

Assessment

Question count not published by the exam provider

Time Limit

Self-paced; about 4 months at roughly 10 hours per week

Passing Score

Course quizzes typically require 70% or higher; all courses and the capstone must be completed

Exam / Certification Fees

Included with a Coursera subscription or Coursera Plus (financial aid available)

Exam sponsor website

Fees, eligibility, and exam policies can change. Confirm them with the exam sponsor before applying or paying.

Our practice resources: topics covered

We aim to reflect publicly available exam outlines and topic information in our study resources. Coverage, format, and difficulty may differ from the actual exam, and we cannot guarantee that every detail is accurate or current. Confirm exam requirements, fees, and policies with the official exam sponsor.

15%

Data Science Methodology and Lifecycle

The Foundational Methodology for Data Science (John Rollins, 10 stages from business understanding through feedback) and the six-phase CRISP-DM framework, problem framing, the analytic approach, analytics types, and why the lifecycle is iterative.

20%

Python for Data Science (NumPy and pandas)

Python data structures, list comprehensions, and exception handling; NumPy arrays and vectorized operations; pandas read_csv, describe, loc/iloc, groupby, merge, value_counts, and missing-value handling; plus APIs with requests and web scraping with BeautifulSoup.

10%

Databases and SQL for Data Science

Writing SQL with SELECT, WHERE, GROUP BY, HAVING, ORDER BY, and DISTINCT; aggregate functions; inner and outer joins; subqueries; and running SQL from Jupyter notebooks against relational databases such as IBM Db2.

13%

Data Analysis and Visualization

Exploratory data analysis, correlation, feature scaling, and encoding; and visualization with Matplotlib, Seaborn, and Folium, including line plots, histograms, scatter plots, box plots, correlation heatmaps, and choropleth maps.

30%

Machine Learning with Python (scikit-learn)

Supervised and unsupervised learning; regression, logistic regression, KNN, decision trees, SVM, and k-means; train/test split, cross-validation, and regularization; and evaluation with R-squared, MSE, accuracy, precision, recall, F1, log loss, ROC/AUC, and the confusion matrix.

12%

Applied Data Science Capstone Concepts

End-to-end project skills demonstrated in the SpaceX Falcon 9 landing capstone: collecting data via API and web scraping, wrangling and EDA, interactive dashboards with Plotly Dash and Folium, comparing classification models with GridSearchCV, and communicating findings.

Preparing for the IBM Data Science Professional Certificate Exam

What You Need to Know

  • Passing score: Course quizzes typically require 70% or higher; all courses and the capstone must be completed
  • Assessment: Question count not published by the exam provider
  • Time limit: Self-paced; about 4 months at roughly 10 hours per week
  • Exam / certification fees: Included with a Coursera subscription or Coursera Plus (financial aid available) Official sources

Using Our Practice Resources

  • Work through all 100 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

IBM Data Science Professional Certificate: Suggested Study Strategy

1Learn the data science methodology cold: be able to name the CRISP-DM phases and the Rollins stages and match each to its activity, since framing the problem before collecting data is a recurring theme.
2Practice pandas hands-on in Jupyter: read_csv, describe, loc vs iloc, groupby, merge, value_counts, dropna, and fillna should feel routine because they appear throughout the analysis courses.
3Drill the difference between supervised and unsupervised learning, and know which algorithm fits which task: regression for continuous targets, classification for labels, and clustering for unlabeled groups.
4Memorize the evaluation metrics and when each matters: R-squared and MSE for regression; accuracy, precision, recall, F1, log loss, and ROC/AUC for classification; and why accuracy misleads on imbalanced data.
5Write SQL by hand: be fluent with GROUP BY versus HAVING, the join types, DISTINCT, and subqueries, and remember WHERE filters rows before grouping while HAVING filters groups after.
6Rebuild the capstone workflow end to end in your head: collect via API and scraping, wrangle, run EDA, build a Plotly Dash and Folium dashboard, tune classifiers with GridSearchCV, and present the findings.

Frequently Asked Questions

Is the IBM Data Science Professional Certificate a single exam?

No. It is a Coursera multi-course professional certificate. Each course is assessed by graded quizzes and hands-on labs, and the program ends with an applied capstone project. There is no single proctored exam or fixed published question count.

What does it cost to take the certificate?

Access is included with a Coursera subscription or Coursera Plus rather than a separate exam fee. Coursera offers financial aid, and learners can often preview the first module of courses for free.

What topics does the program cover?

It covers data science methodology and CRISP-DM, Python with NumPy and pandas, databases and SQL, data analysis and visualization with Matplotlib, Seaborn, and Folium, machine learning with scikit-learn, a generative AI course, and an applied capstone.

Do I need prior experience to enroll?

No. The certificate is beginner-friendly and requires no prior programming or data science experience. It builds skills from foundations to a portfolio-ready capstone, making it suitable for career changers.

How long does the certificate take to complete?

Coursera estimates about four months at roughly ten hours per week, though the program is self-paced. Learners can move faster or slower depending on their schedule and background.

What credential do I earn at the end?

You earn the IBM Data Science Professional Certificate plus an IBM digital badge issued through Credly. The badge lists demonstrated skills such as Python, SQL, and machine learning and can be shared on LinkedIn and resumes.