Section 1: Data Analytics Fundamentals (Q1–Q10)
1. What is the primary role of a Data Analyst?
A) Build ML models
B) Interpret data for decisions
C) Design hardware
D) Manage servers
Answer: B
Solution: Data Analysts extract and interpret data to guide decision-making.
2. Structured data is stored in:
A) Images
B) Videos
C) Tables
D) Audio files
Answer: C
3. KPI stands for:
A) Key Performance Indicator
B) Key Process Index
C) Knowledge Performance Item
D) Key Predictive Insight
Answer: A
4. ETL stands for:
A) Extract, Transform, Load
B) Export, Transfer, Load
C) Extract, Transfer, Link
D) Enter, Transform, Load
Answer: A
5. Data cleaning involves:
A) Deleting all data
B) Removing errors/inconsistencies
C) Encrypting data
D) Compressing files
Answer: B
6. A dashboard primarily helps in:
A) Coding
B) Data storage
C) Data visualization
D) Server security
Answer: C
7. Example of unstructured data:
A) Excel sheet
B) SQL table
C) Tweets
D) CSV file
Answer: C
8. Data-driven decisions rely on:
A) Assumptions
B) Opinions
C) Evidence
D) Guesswork
Answer: C
9. A primary key must be:
A) Duplicate
B) Unique
C) Null
D) Optional
Answer: B
10. OLAP stands for:
A) Online Analytical Processing
B) Online Linear Analysis Program
C) Operational Logic Analysis
D) None
Answer: A
Section 2: Statistics & Probability (Q11–Q20)
11. Mean is sensitive to:
A) Mode
B) Median
C) Outliers
D) Frequency
Answer: C
12. Median represents:
A) Average
B) Middle value
C) Highest value
D) Spread
Answer: B
13. Standard deviation measures:
A) Central value
B) Spread
C) Correlation
D) Bias
Answer: B
14. Variance is:
A) Square root of SD
B) Square of SD
C) Mean
D) Mode
Answer: B
15. Probability range is:
A) 0–1
B) 1–100
C) -1–1
D) 0–10
Answer: A
16. If p < 0.05:
A) Accept null
B) Reject null
C) Ignore data
D) None
Answer: B
17. Correlation value range:
A) 0–1
B) -1 to 1
C) 0–10
D) -10 to 10
Answer: B
18. Normal distribution is:
A) Skewed
B) Bell-shaped
C) Flat
D) Random
Answer: B
19. Z-score measures:
A) Probability
B) Distance from mean
C) Median
D) Spread
Answer: B
20. Type I error is:
A) False positive
B) False negative
C) True positive
D) True negative
Answer: A
Section 3: SQL (Q21–Q30)
21. Filter records using:
A) ORDER BY
B) WHERE
C) GROUP BY
D) SELECT
Answer: B
22. Count rows:
A) SUM()
B) TOTAL()
C) COUNT()
D) NUMBER()
Answer: C
23. Combine tables:
A) JOIN
B) MERGE
C) LINK
D) UNION
Answer: A
24. Remove duplicates:
A) UNIQUE()
B) DISTINCT
C) DELETE
D) FILTER
Answer: B
25. Sort results:
A) ORDER BY
B) SORT()
C) GROUP BY
D) SELECT
Answer: A
26. Group records:
A) ORDER BY
B) GROUP BY
C) WHERE
D) JOIN
Answer: B
27. Filter grouped data:
A) WHERE
B) HAVING
C) ORDER BY
D) SELECT
Answer: B
28. Primary key ensures:
A) Uniqueness
B) NULLs allowed
C) Duplicate values
D) None
Answer: A
29. Foreign key:
A) Unique per row
B) Links tables
C) Stores duplicates
D) Encrypts data
Answer: B
30. INNER JOIN returns:
A) All rows
B) Matching rows only
C) Left table rows only
D) Right table rows only
Answer: B
Conclusion.
In conclusion, mastering Data Analyst multiple-choice questions (MCQs) is an effective way to strengthen your understanding of core data analytics concepts and prepare for interviews, certification exams, and competitive tests. These questions typically cover essential areas such as statistics, probability, data cleaning, exploratory data analysis (EDA), SQL, Excel, Python, data visualization, and business intelligence tools.
Practicing MCQs helps reinforce theoretical knowledge, improve problem-solving speed, and identify gaps in understanding. It also builds familiarity with real-world scenarios where data analysts must interpret results, choose appropriate methods, and make data-driven decisions.
To excel as a data analyst, consistent practice combined with hands-on experience using tools like Excel, SQL, Python, and visualization platforms is crucial. A strong foundation in analytical thinking, logical reasoning, and data interpretation will significantly enhance career growth in the data analytics field.
Overall, regular practice of well-structured MCQs not only boosts confidence but also prepares aspiring professionals to meet industry expectations and succeed in data-driven roles.
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