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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
You are tasked with preparing a large dataset of website clickstream data stored in a Snowflake table named This table contains a 'timestamp' column (TIMESTAMP NTZ), a column (VARCHAR), and a 'page_urr column (VARCHAR). You need to identify the most popular pages visited by users during specific hours of the day, but only for users who have visited at least 5 unique pages in the dataset. Which sequence of SQL operations, potentially including temporary tables or CTEs, would efficiently achieve this goal in Snowflake?

Answer: B,C

Explanation:
Option B and E are the most efficient solutions. Option B uses a CTE (Common Table Expression) to filter users who have visited at least 5 unique pages and then joins this result with the original table to calculate visit counts by hour and page. Option E creates a TEMP table for qualified users then joins against it to create a final aggregated set. A is functionally correct but may not be as optimized as using a CTE and uses two queries. C has an unnecessary subquery in the WHERE clause, which can degrade performance. D uses inline view which is less readable and less efficient than CTE. Both CTEs (B) and temporary tables (E) are common techniques in Snowflake for breaking down complex queries and can improve readability and maintainability. Snowflake's query optimizer is generally very good, and the CTE approach (B) is often preferred for its clarity.


NEW QUESTION # 34
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 # 35
Which statement accurately describes the use of regular views in data analysis?

Answer: B

Explanation:
Regular views aid analysis by simplifying complex data structures, improving comprehension.


NEW QUESTION # 36
A data analyst is tasked with optimizing a daily ETL pipeline that loads data from several external sources into a Snowflake data warehouse. One of the key transformations involves joining two large tables, 'ORDERS' (millions of rows) and 'CUSTOMERS' (hundreds of thousands of rows), based on 'CUSTOMER ID. The pipeline currently uses a standard 'JOIN' operation, but the transformation step is taking longer than expected. The analyst has explored various optimization techniques, including increasing virtual warehouse size, but the performance improvement is minimal. Assuming that the 'CUSTOMER ID column is appropriately indexed (or clustered, if applicable) in both tables, and you want to minimise data movement. Which of the following approaches would yield the MOST significant performance improvement for this transformation step, considering metadata caching and data distribution?

Answer: B

Explanation:
Ensuring consistent data types and collations (D) is crucial for optimal join performance in Snowflake. When data types and collations match, Snowflake can leverage its internal optimizations more effectively, including metadata caching and efficient data access patterns. Using a 'BROADCAST JOIN' hint without considering data distribution (A) might lead to unnecessary data movement and performance degradation. 'MAP JOIN' is unavailable in Snowflake (B). Redistributing data using 'DISTRIBUTION_TYPE = HASH' (C) is generally less efficient than leveraging Snowflake's automatic optimizations. Pre-sorting data (E) is unnecessary in Snowflake as it does not guarantee data colocation in the same way that other distributed systems might.


NEW QUESTION # 37
How does using Snowsight's data loading capabilities impact the overall data preparation process?

Answer: D

Explanation:
Snowsight's data loading capabilities streamline the process, reducing preparation time by facilitating efficient data loading.


NEW QUESTION # 38
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