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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
You are tasked with creating a dashboard to monitor the performance of several marketing campaigns. The dashboard should allow users to quickly identify underperforming campaigns and drill down to understand the reasons. Which of the following combinations of visualizations and data interaction techniques would BEST achieve this goal? Assume that you are using Snowflake as your data source and connecting to a BI tool.

Answer: B,E

Explanation:
Options B and E provide the best combination of visualizations and data interaction techniques. Option B uses a bar chart for easy comparison and cross-filtering to allow for quick drill-down into related data dimensions. Option E utilizes a bullet chart to immediately highlight underperforming campaigns relative to their targets and offers a drill-down for a more in-depth analysis of underlying metrics and customer data. Option A lacks drill-down. Option C is useful for understanding distribution but does not highlight performance against targets or facilitate drill-down. Option D, although useful can quickly see the campaign's performance in one glance, it does not provide in-depth insights into why the campaign isn't performing well.


NEW QUESTION # 54
You are troubleshooting a slow-running query that performs a complex transformation on a large dataset using a series of CTEs. You've identified one particular CTE, , as contributing significantly to the overall execution time. Examination of the query profile reveals high spillover to local storage during the execution of this CTE. Which of the following actions could potentially reduce spillover and improve the performance of the CTE? Select all that apply.

Answer: B,C

Explanation:
Increasing the warehouse size (A) provides more memory, which directly reduces spillover. Rewriting the CTE to use smaller intermediate tables (C) reduces the memory footprint of the operation. Partitioning the tables (B) is not directly supported in Snowflake. Creating a materialized view from the CTE (D) can improve performance if the CTE's results are frequently reused, but it does not necessarily address the spillover issue directly. Reducing the 'MAX (E) would worsen spillover. Using temporary tables (E) will not necessarily impact spilling if the memory limits are still exceeded within the query runtime.


NEW QUESTION # 55
You have a table 'PRODUCTS' with a 'PRICE' column stored as VARCHAR. Some values in this column are valid numerical strings (e.g., '12.99'), while others contain invalid characters (e.g., '12.99USD', 'N/A'). You need to calculate the average price of all valid products. Which of the following approaches ensures that you only consider valid numeric values and handles potential errors effectively? Select all that apply.

Answer: D,E

Explanation:
Option B is correct because it uses a regular expression to explicitly check if the PRICE column contains only valid numeric characters (digits and an optional decimal point) before attempting to cast it to a DECIMAL. 'REGEXP LIKE' ensures that non-numeric values are filtered out, preventing errors during casting, and then 'CAST is used since it is validated by the regular expression check. Option D is also correct because it uses 'CASE statement with 'IS DECIMAL' function that is a user defined function (UDF) for checking if a string can be converted to decimal, and if True, it casts the PRICE to DECIMAL; otherwise, it assigns NULL. The AVG function automatically ignores NULL values, ensuring that only valid numeric values are considered. Option A won't work as DECIMAL' function is not standard, so you will need to create user defined function. 'TO_NUMBER and 'TO_DECIMAL' throws error if it cannot parse which is not ideal. Option E, throws error if column PRICE cannot be converted, where as 'TRY TO DECIMAL' will not throw the error and return NULL.


NEW QUESTION # 56
You are tasked with creating a new data model in Snowflake for a marketing analytics team. The source data is in a 3rd Normal Form (3NF) relational database. The team requires fast query performance for ad-hoc analysis and dashboards, primarily focusing on sales trends by product category, region, and customer segment. Which of the following approaches is MOST effective for transforming the 3NF data into a consumption-ready layer in Snowflake?

Answer: C

Explanation:
A star schema is optimized for Bl querying due to its denormalized structure and clear separation of facts and dimensions. Clustering the fact table on date further improves query performance for time-based trend analysis. Replicating the 3NF structure would not provide the necessary performance for ad-hoc analysis. Data Vault, while beneficial for long-term data management, adds complexity and overhead for this specific use case. A single wide table can lead to performance issues with a large number of columns. While materialized views can help, a star schema provides a more fundamental structure optimized for the analysis patterns described.


NEW QUESTION # 57
You are tasked with loading a large CSV file containing website traffic data into Snowflake. The CSV file has the following characteristics: Header row is present. Fields are enclosed in double quotes. The delimiter is a pipe (l) character. One column, 'timestamp' , is stored as milliseconds since the epoch and needs to be converted to a Snowflake TIMESTAMP NTZ. Which of the following COPY INTO statement options would correctly load the data, handle the delimiter and quotes, and convert the 'timestamp' column?

Answer: C

Explanation:
Option E is correct because it correctly handles the milliseconds since epoch conversion. It casts the 'timestamp' column to a to ensure accurate division and then divides by 1000 to convert milliseconds to seconds before applying TO_TIMESTAMP_NTZ. Option A is incorrect because timestamp could be a String. Option B is incorrect as date format is not relevant here and it doesn't divide by 1000. Option C is incorrect because BIGINT might not be sufficient for large timestamps. Option D is incorrect because = 'EPOCH MILLIS" is used in file format options, not transform column.


NEW QUESTION # 58
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