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1. What is the key difference between being data-informed and data-driven?
A) Data-driven decisions balance intuition with insights, but prioritize context over data. B) Data-informed decisions rely only on intuition, ignoring metrics and analysis. C) Data-driven decisions focus solely on evidence, while data-informed decisions balance data with context and experience. D) Data-informed decisions balance data with context and experience, rather than relying solely on numbers.
2. What makes a metric actionable in a metrics tree?
A) A metric that is static and reflects past performance only. B) A metric that provides clear levers to drive product improvement and aligns with broader goals. C) A metric that is easy to understand and quick to explain to stakeholders. D) A metric that gets attention in dashboards due to strong visual representation.
3. What is the purpose of a power analysis in A/B testing?
A) To determine how many users are needed to ensure statistically significant results for a given metric. B) To calculate the overall cost of running the test over a fixed duration. C) To identify which variant will perform best before starting the test. D) To visualize cohort behavior and remove outliers in test results.
4. Why should you avoid drawing early conclusions during an A/B test?
A) Early conclusions ensure you meet strict deadlines, regardless of data accuracy. B) Early conclusions can lead to faster decision-making but may lack accuracy. C) Early conclusions often reflect incomplete data, leading to unreliable results and potential errors. D) Early conclusions are safe if test groups are properly randomized.
5. What is the purpose of understanding table granularity?
A) To understand what each row in a table represents, ensuring correct data interpretation. B) To identify the hierarchical relationships between different databases. C) To optimize the column order in a table for faster query processing. D) To choose the best table structure for data visualization.
6. How can you determine a table's primary key?
A) By analyzing the granularity level of the table and selecting the column with the most null values. B) By counting the distinct values in all columns and selecting the one with the fewest duplicates. C) By identifying the column (or combination of columns) that uniquely identifies each row in the table. D) By checking the column with the highest cardinality in a table.
7. What is the purpose of root-cause analysis (RCA)?
A) To identify underlying causes of a problem and propose possible solutions for improvement. B) To validate assumptions made during product development. C) To evaluate past successes and focus on repeating them without examining failures. D) To uncover patterns of user engagement across different product areas.
8. What is a key step when defining hypotheses during RCA?
A) Ensure that each hypothesis directly supports the chosen solution. B) Hypotheses should cover user experience, product offerings, audience behavior, and external events. C) Always focus only on the most recent product change as the root cause. D) Define KPIs for each root cause before gathering supporting data.
9. Why is joining tables important in data analysis?
A) To split large tables into smaller segments for visualization. B) To create redundancy across systems for backup purposes. C) To combine related information across tables for a more complete dataset. D) To separate transactional data from user attributes for speed.
10. What is the use of the HAVING clause in SQL?
A) To filter individual rows based on column values. B) To limit the number of results after sorting. C) To filter aggregated results after using GROUP BY. D) To group similar values into buckets.
