Valid Test DAA-C01 Bootcamp - DAA-C01 Training For Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
When employing window functions versus table functions in Snowflake, how do they differ in their application and output?

Answer: B

Explanation:
Table functions in Snowflake process data within specified partitions or frames, whereas window functions generate aggregate results and operate on entire datasets, differing in their scope and operation.


NEW QUESTION # 63
A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?

Answer: B

Explanation:
In Data Transformation and Data Modeling, selecting the correct join type determines how the query handles unmatched records from the participating tables. The requirement here is to return all records from both the projects table and the employee table.
A FULL OUTER JOIN (often shortened to FULL JOIN) is designed specifically for this purpose. It combines the results of both a LEFT OUTER JOIN and a RIGHT OUTER JOIN. It returns:
* Rows where there is a match between the project and the employee.
* Rows from the projects table that have no matching employees (padded with NULL in the employee columns).
* Rows from the employees table that are not assigned to any projects (padded with NULL in the project columns).
Evaluating the Options:
* Option A (INNER JOIN) only returns rows where there is a match in both tables. Any projects without employees or employees without projects would be excluded.
* Option C (LEFT OUTER JOIN) would return all projects, but would exclude employees who are not assigned to a project.
* Option D (CROSS JOIN) creates a Cartesian product, matching every single employee with every single project regardless of actual relationships. This would create a massive, redundant dataset and is not what is requested.
* Option B is the 100% correct answer. It ensures total data visibility from both entities, which is often required in data quality audits or comprehensive resource allocation reporting where the analyst needs to see "unlinked" data on both sides of the relationship.


NEW QUESTION # 64
You are analyzing website traffic data in Snowflake. The 'web_events' table contains 'event_timestamp' (TIMESTAMP N T Z), 'user_id', and 'page_url'. You discover that many 'event_timestamp' values are significantly skewed towards the future (e.g., a year ahead), likely due to incorrect device clocks. You want to correct these skewed timestamps by assuming the majority of events are valid and calculating a time drift. Which of the following strategies using Snowflake functionality would be MOST efficient and accurate for correcting these timestamps?

Answer: B

Explanation:
Option D provides the most robust approach. Using the median minimizes the impact of outliers (future-dated timestamps). Calculating the difference between each event timestamp and the overall median timestamp isolates the 'time_drift' for each record, which is then subtracted from each future skewed events. Option A uses median for each user, which is unneccesary. Options B and E are vulnerable to outliers (the very problem we're trying to solve). Option C, while conceptually interesting, isn't directly supported as a native aggregate function for timestamps in most SQL dialects, including Snowflake, without custom user-defined functions (UDFs), making it less efficient and potentially less accurate.


NEW QUESTION # 65
A retail company is analyzing sales data to optimize product placement and promotional campaigns. They have sales figures, customer demographics, and promotional campaign details stored in Snowflake. Which visualization technique and Snowflake feature combination would BEST help them identify nuanced correlations between customer age, product category, and the success rate of different promotional campaigns, allowing for interactive exploration and drill-down capabilities?

Answer: E

Explanation:
Option B is the most appropriate because it leverages an interactive dashboard (Streamlit) connected to Snowflake, enabling cross- filtering and drill-down capabilities. Using stored procedures in Snowflake to pre-calculate aggregated data enhances dashboard performance. Option A provides a static view, lacking interactivity. Option C relies on a static table, which is not ideal for exploratory analysis. Option D involves exporting data outside Snowflake, which is inefficient and less secure. Option E might not provide the specific visualizations or level of customization needed for detailed correlation analysis. The use of the heatmap allows a clearer correlation view.


NEW QUESTION # 66
You are a data analyst at a retail company. You need to calculate the total sales for each product category, but only for categories where the average unit price is above $50 and the number of sales transactions exceeds 1000. Which Snowflake SQL query would efficiently achieve this?

Answer: E

Explanation:
Option D correctly uses the HAVING clause to filter aggregated results based on both the average unit price and the count of transactions. Option A does not consider distinct transaction_id, Option B includes the aggregate functions in where clause which is not valid, option C uses subquery which adds overhead, option E uses QUALIFY that is not suitable to this scenario.


NEW QUESTION # 67
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