Page 1 of 66
SUMMARY DATA SCIENCE EXAM PREP 2026 SOLVED QUESTIONS & ANSWERS WITH
DETAILED RATIONALES VERIFIED 100 %
Summary Data Science — 250 Practice Questions with Detailed Rationales
1. Data science is best defined as:
A) Only building machine learning models
B) Extracting insights from data using statistics, programming, and domain knowledge
C) Managing databases only
D) Creating dashboards only
Rationale: Data science combines statistics, programming, and domain expertise to
extract actionable insights from data.
2. Which lifecycle model is commonly used in data science projects?
A) CRISP-DM
B) OSI model
C) TCP/IP model
D) Waterfall only
Rationale: CRISP-DM (Cross-Industry Standard Process for Data Mining) is a widely
used data science lifecycle.
, Page 2 of 66
3. The first phase of CRISP-DM is:
A) Data preparation
B) Business understanding
C) Modeling
D) Deployment
Rationale: CRISP-DM begins with business understanding, defining objectives and
requirements.
4. A data scientist’s role typically includes:
A) Only data entry
B) Data wrangling, modeling, and communication
C) Only hardware maintenance
D) Only network security
Rationale: Data scientists gather, clean, model, and communicate data-driven
insights.
5. Which is NOT a core skill for data science?
A) Statistics
B) Programming
C) Domain knowledge
D) Plumbing
Rationale: Plumbing is not a core data science skill; statistics, programming, and
domain knowledge are.
6. Data science differs from traditional statistics mainly by:
A) Ignoring inference
B) Emphasis on prediction and large-scale computing
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C) Avoiding data
D) Using only spreadsheets
Rationale: Data science often emphasizes prediction, large data, and computing
alongside statistical inference.
7. A data pipeline is:
A) A sequence of data processing steps
B) A type of chart
C) A statistical test
D) A database only
Rationale: A data pipeline moves and transforms data from source to analysis or
deployment.
8. Which question is best framed as a data science problem?
A) What is the meaning of life?
B) Which customers are likely to churn next month?
C) What is the capital of France?
D) How to fix a car engine?
Rationale: Churn prediction is measurable, data-driven, and actionable.
9. The “I” in CRISP-DM does not stand for:
A) Iteration
B) Implementation
C) Integration
D) Investigation
Rationale: CRISP-DM phases are Business Understanding, Data Understanding, Data
Preparation, Modeling, Evaluation, Deployment.
, Page 4 of 66
10. Reproducibility in data science means:
A) Results can be reproduced by others
B) Data is always public
C) Models are never changed
D) Only one person can run code
Rationale: Reproducibility ensures analyses can be independently repeated.
11. A data product is:
A) A physical product only
B) A tool or system that uses data to deliver value
C) A database table
D) A spreadsheet
Rationale: Data products include dashboards, APIs, recommendation systems, and
predictive models.
12. Which is a common data science team role?
A) Data engineer
B) Data analyst
C) ML engineer
D) All of the above
Rationale: Data teams often include engineers, analysts, scientists, and ML engineers.
13. The main goal of exploratory data analysis is:
A) Confirm hypotheses only
B) Understand data patterns before modeling
C) Deploy models
D) Write reports
SUMMARY DATA SCIENCE EXAM PREP 2026 SOLVED QUESTIONS & ANSWERS WITH
DETAILED RATIONALES VERIFIED 100 %
Summary Data Science — 250 Practice Questions with Detailed Rationales
1. Data science is best defined as:
A) Only building machine learning models
B) Extracting insights from data using statistics, programming, and domain knowledge
C) Managing databases only
D) Creating dashboards only
Rationale: Data science combines statistics, programming, and domain expertise to
extract actionable insights from data.
2. Which lifecycle model is commonly used in data science projects?
A) CRISP-DM
B) OSI model
C) TCP/IP model
D) Waterfall only
Rationale: CRISP-DM (Cross-Industry Standard Process for Data Mining) is a widely
used data science lifecycle.
, Page 2 of 66
3. The first phase of CRISP-DM is:
A) Data preparation
B) Business understanding
C) Modeling
D) Deployment
Rationale: CRISP-DM begins with business understanding, defining objectives and
requirements.
4. A data scientist’s role typically includes:
A) Only data entry
B) Data wrangling, modeling, and communication
C) Only hardware maintenance
D) Only network security
Rationale: Data scientists gather, clean, model, and communicate data-driven
insights.
5. Which is NOT a core skill for data science?
A) Statistics
B) Programming
C) Domain knowledge
D) Plumbing
Rationale: Plumbing is not a core data science skill; statistics, programming, and
domain knowledge are.
6. Data science differs from traditional statistics mainly by:
A) Ignoring inference
B) Emphasis on prediction and large-scale computing
, Page 3 of 66
C) Avoiding data
D) Using only spreadsheets
Rationale: Data science often emphasizes prediction, large data, and computing
alongside statistical inference.
7. A data pipeline is:
A) A sequence of data processing steps
B) A type of chart
C) A statistical test
D) A database only
Rationale: A data pipeline moves and transforms data from source to analysis or
deployment.
8. Which question is best framed as a data science problem?
A) What is the meaning of life?
B) Which customers are likely to churn next month?
C) What is the capital of France?
D) How to fix a car engine?
Rationale: Churn prediction is measurable, data-driven, and actionable.
9. The “I” in CRISP-DM does not stand for:
A) Iteration
B) Implementation
C) Integration
D) Investigation
Rationale: CRISP-DM phases are Business Understanding, Data Understanding, Data
Preparation, Modeling, Evaluation, Deployment.
, Page 4 of 66
10. Reproducibility in data science means:
A) Results can be reproduced by others
B) Data is always public
C) Models are never changed
D) Only one person can run code
Rationale: Reproducibility ensures analyses can be independently repeated.
11. A data product is:
A) A physical product only
B) A tool or system that uses data to deliver value
C) A database table
D) A spreadsheet
Rationale: Data products include dashboards, APIs, recommendation systems, and
predictive models.
12. Which is a common data science team role?
A) Data engineer
B) Data analyst
C) ML engineer
D) All of the above
Rationale: Data teams often include engineers, analysts, scientists, and ML engineers.
13. The main goal of exploratory data analysis is:
A) Confirm hypotheses only
B) Understand data patterns before modeling
C) Deploy models
D) Write reports
