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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
A data analyst is building a dashboard in Tableau connected to Snowflake. The dashboard needs to display the average order value (AOV) for each product category over the last quarter. The Snowflake table 'ORDERS contains columns: 'ORDER ID' , 'ORDER DATE', 'PRODUCT CATEGORY' , and 'ORDER VALUE'. The analyst wants to minimize data transfer from Snowflake to Tableau and optimize dashboard performance. Which of the following approaches is MOST efficient?

A. Create a Snowflake view that calculates the AOV and connect Tableau to the view.
B. Create a Snowflake stored procedure to calculate the AOV and call the stored procedure from Tableau using ODBC connection.
C. Extract all data from the 'ORDERS' table into Tableau and calculate the AOV within Tableau.
D. Use Tableau's Custom SQL feature to execute a query that calculates the AOV directly in Snowflake and retrieves only the aggregated results.
E. Schedule a daily task to export AOV data from Snowflake into a CSV file, and use Tableau to read from the CSV file.

Answer: A,D

Explanation:
Both using Tableau's Custom SQL and creating a Snowflake view are efficient options. Custom SQL allows pushing the calculation to Snowflake, minimizing data transfer and leveraging Snowflake's compute power. Creating a view is also a very good approach since it pre-calculates and store the result set as a view. Extracting all data into Tableau is inefficient due to high data transfer. Using a CSV file adds complexity and latency. Stored procedures are typically not designed for direct dashboard interaction; they are better suited for data transformation pipelines.

NEW QUESTION # 97
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?

Answer: B

Explanation:
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.

NEW QUESTION # 98
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.

A. A table showing the campaign name, total cost, and total revenue, sorted by revenue in descending order. No drill-down capabilities are included.
B. A bullet chart comparing actual revenue to target revenue for each campaign, sorted by the difference between actual and target. Implement drill-down capabilities to view campaign-specific metrics (e.g., cost per acquisition, conversion rate) and associated customer data.
C. Apie chart showing the percentage of total revenue generated by each campaign.
D. A scatter plot showing campaign cost vs. revenue, without any filters or drill-down capabilities.
E. Abar chart comparing the revenue generated by each campaign, with a filter to select a specific date range. Implement cross-filtering such that selecting a bar filters other charts on the dashboard (e.g., demographic distribution, geographic distribution) to show data related to that campaign.

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 # 99
You have a large dataset in Snowflake containing customer order information stored in a table named 'ORDERS' with columns 'ORDER_ID' ONT), 'CUSTOMER_ID' ONT), 'ORDER_DATE (DATE), 'TOTAL_AMOUNT' (FLOAT), and 'DISCOUNT_APPLIED' (BOOLEAN). You need to use Snowsight dashboards to analyze customer spending behavior and identify potential outliers. Which of the following visualizations, combined with appropriate SQL queries, would be MOST effective in identifying customers with unusually high or low order values? (Select TWO)

A. Option D
B. Option A
C. Option E
D. Option B
E. Option C

Answer: B,E

Explanation:
Options A and C are the most effective. A Box Plot (A) is ideal for identifying outliers in a distribution. By visualizing the distribution of total order amounts per customer, you can easily spot customers with unusually high or low spending. A Scatter Plot (C) directly shows the relationship between customer ID and total spending, making it easy to visually identify outliers based on their position relative to other data points. Option B is more suitable for trend analysis over time, and options D and E are useful but don't directly highlight individual customer outliers in terms of order value.

NEW QUESTION # 100
You're building a dashboard to monitor the performance of various marketing campaigns. The data resides in Snowflake, and you're using a BI tool that supports direct query The table has columns: 'CAMPAIGN ID, 'DATE, 'IMPRESSIONS', 'CLICKS, 'SPEND , and You need to create a calculated field in the BI tool representing the Cost Per Conversion (CPC), but you want to optimize query performance and avoid division by zero errors. Assume 'SPEND' and 'CONVERSIONS' are both numeric columns. Which SQL expression, suitable for use in a direct query BI tool, is the MOST performant and robust way to calculate CPC, avoiding zero conversion issues?

Answer: E

Explanation:
The ' DIV0' function is specifically designed by Snowflake to handle division by zero gracefully, returning NULL. It's the most concise and performant way to achieve the desired result. 'NULLIF(CONVERSIONS, 0)' is also correct way, but DIVO is more accurate for Snowflake environment. 'CASE WHEN' and 'IFF are functionally equivalent in this scenario, but is shorter to write. ZEROIFNULL' will return 0 when input is null which won't solve the zero conversion issues. Furthermore ZEROIFNULL' is not a valid Snowflake function.

NEW QUESTION # 101
......

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TEST SNOWFLAKE DAA-C01 DUMP - DAA-C01 DOWNLOAD PDF

Test Snowflake DAA-C01 Dump - DAA-C01 Download Pdf