Snowflake DAA-C01 Valid Exam Cram, DAA-C01 Original Questions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q253-Q258):

NEW QUESTION # 253
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

A. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.
B. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.
C. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.
D. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
E. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.

Answer: A

Explanation:
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

NEW QUESTION # 254
You have a Snowflake table named 'sensor_data' with a column 'reading' containing JSON data'. The JSON structure varies, but you want to extract a specific nested value, 'temperature', using a UDE The path to 'temperature' might be different depending on the 'sensor_type'. Some sensors have the temperature at '$.metrics.temperature' , others at '$.reading.temp_c'. The sensor type is stored in the 'sensor_type' column. You want to create a UDF named which takes the JSON 'reading' and the 'sensor_type' as input and extracts the temperature, returning NULL if the path does not exist in the JSON. How can you implement this using a JavaScript UDF and Snowflake's JSON parsing functions for optimal performance?

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

Answer: B

Explanation:
Option C is the MOST optimal. It uses 'VARIANT as the input data type for 'reading' , which avoids the unnecessary parsing of JSON using as Snowflake automatically parses JSON data into a VARIANT type. It also includes a 'try...catch' block to handle cases where the specified path does not exist within the JSON, returning 'NULL' as required. This prevents errors from halting the query. Using 'VARIANT directly and exception handling offers superior performance. Option A and D parse VARIANT as String, leading to parsing overhead. B misses the try catch block and is prone to failure when temp is not available for a given sensor. Option E is less efficient than option C due to using array notation.

NEW QUESTION # 255
You are building a Snowsight dashboard to monitor the performance of various SQL queries. You have a table 'QUERY HISTORY with columns 'QUERY ID', 'START TIME, 'END TIME, 'USER NAME, 'DATABASE NAME, and 'EXECUTION_TIME (in seconds). You want to create a bar chart that shows the average execution time for each user, but only for queries executed against a specific database (e.g., 'SALES DB') within the last week. Furthermore, you need to allow users to filter the data by username via a Snowsight dashboard variable. What is the most efficient SQL query and Snowsight configuration to achieve this?

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

Answer: B

Explanation:
Option B provides the most efficient solution by using a Snowsight dashboard variable ('USERNAME) with a dropdown populated from the distinct usernames in the 'QUERY HISTORY table. This allows users to easily filter the data by selecting a username from the dropdown, and the SQL query directly incorporates the variable using '$USERNAME in the WHERE clause for optimal performance. Option A lacks the dynamic filtering capability. Option C will not work as 'HAVING' clause should use aggregate functions, and even if it worked is not efficient. Option D would be difficult to maintain and less performant. Option E introduces unnecessary complexity with a stored procedure for this specific requirement.

NEW QUESTION # 256
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)

Answer: A,E

Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.

NEW QUESTION # 257
Consider a Snowflake table 'LOG ENTRIES' with a VARIANT column 'LOG DATA' containing log messages with potentially varying structures. Some logs contain error details under the key 'error', while others do not. If 'error' exists, it contains a nested object with 'code' and 'message'. You want to extract the error 'message' when it exists, otherwise return 'No Error'. Which query accomplishes this with optimal performance?

Answer: D

Explanation:
Option D is the most robust and efficient solution. 'IF-NULLS handles cases where 'LOG DATA:error:message' is NULL (either because 'error' doesn't exist or 'message' is NULL). The explicitly casts the value to a string, ensuring consistent data type. Options A and B will return NULL when the 'error' object or the 'message' key doesn't exist without handling the null replacement. Option C will error as it does not cast value into a String from a variant data type. Option E is not valid as TRY_TO_STRING function only accepts one argument

NEW QUESTION # 258
......

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SNOWFLAKE DAA-C01 VALID EXAM CRAM, DAA-C01 ORIGINAL QUESTIONS

Snowflake DAA-C01 Valid Exam Cram, DAA-C01 Original Questions