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Quiz 2025 Accurate Snowflake DAA-C01: Valid SnowPro Advanced: Data Analyst Certification Exam Exam Sims

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q238-Q243):

NEW QUESTION # 238
You have a Snowflake table named 'CUSTOMER DATA' with a VARIANT column called 'PROFILE. This 'PROFILE column contains nested JSON objects with customer attributes. You need to extract the customer's first name from the 'PROFILE and handle cases where the 'firstName' field might be missing (NULL). Which of the following methods is the most efficient and concise way to achieve this?

Answer: D

Explanation:
Option E is the most efficient and concise. 'PROFILE:firstName' directly accesses the field, casts it to a string and 'IFNI-ILL' handles the potential NULL values, replacing them with 'Unknown'. Options A and C might return NULL if the field is missing. Option B is less efficient than direct path access. While option D works, casting to string is essential to ensure the correct datatype and to avoid any unexpected behaviors, making option E the ideal answer.

NEW QUESTION # 239
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?

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

Answer: A

Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.

NEW QUESTION # 240
You are building a dashboard in Power BI that connects to Snowflake. The dashboard needs to display the trend of daily active users (DAU) for the past year. The 'USER_ACTIVITY table in Snowflake contains columns: 'USER ONT), 'ACTIVITY DATE (DATE), and 'ACTIVITY TYPE (VARCHAR). Due to the large size of the 'USER ACTIVITY table, query performance is critical. Which of the following strategies will BEST optimize the query executed by Power BI against Snowflake to calculate DAU?

A. Directly query the 'USER_ACTIVITY table from Power BI using a DAX measure to calculate distinct user counts per day. Rely on Power BI'S query folding capabilities to optimize the query sent to Snowflake.
B. Using a scheduled task in Snowflake, regularly create a summary table containing daily active users, then connect Power BI to that summary table.
C. Import the entire 'USER ACTIVITY table into Power BI using Power BI Desktop's data import functionality and calculate DAU within Power BI'S data model.
D. Create a Snowflake view that calculates the DAU for each day using 'COUNT(DISTINCT USER_ID)' and 'GROUP BY ACTIVITY_DATE' , then connect Power BI to this view.
E. Create a materialized view in Snowflake that pre-calculates the DAU for each day, then connect Power BI to this materialized view.

Answer: E

Explanation:
Creating a materialized view that pre-calculates the DAU for each day is the most effective approach for optimizing query performance. Materialized views store the results of the query, so Power BI only needs to retrieve the pre-calculated DAU values, avoiding the expensive 'COUNT(DISTINCTY operation on the entire 'USER_ACTIVITY table. Option A might not fold completely and still execute poorly. Option B is better than A but does not provide performance like a Materialized View. Option D brings all the data into Power 31, which is not scalable or efficient. Option E is a valid option but has additional management overhead compared to materialized views.

NEW QUESTION # 241
You're tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called 'ETL LOGS' with columns 'end_time', and 'error_flag' (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?

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

Answer: B

Explanation:
Option A is the most accurate. It correctly calculates the average run time in seconds using start_time, end_time)' and the error count using a conditional aggregation 'SUM(CASE WHEN error_flag THEN 1 ELSE 0 END)'. Option B is incorrect because - start_timey will not return runtime in seconds, it returns a fractional number of days. Option C uses IFF and datediff which is acceptable. Option D only counts the total logs and doesn't filter if there is an error. Option E is also technically correct by converting the boolean to a number to sum. Option A uses standard SQL which might be more preferable.

NEW QUESTION # 242
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?

A. Creating a view with direct JSON path accessors (e.g., for each desired attribute.
B. Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.
C. Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.
D. Creating a new table with a 'VARIANT column for the attributes and performing transformations in a BI tool.
E. Using LATERAL FLATTEN to unnest the 'attributes' and then using a CASE statement to pivot the data.

Answer: A

Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.

NEW QUESTION # 243
......

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