CERTIFICATION DAA-C01 DUMPS - 100% LATEST QUESTIONS POOL

Certification DAA-C01 Dumps - 100% Latest Questions Pool

Certification DAA-C01 Dumps - 100% Latest Questions Pool

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q187-Q192):

NEW QUESTION # 187
A retail company wants to build a dashboard to track sales performance by region. They have implemented Dynamic Data Masking on the 'CUSTOMER PHONE' column in their 'CUSTOMERS' table. A user with the 'ANALYST ROLE' needs to see the sales data and masked phone numbers in the dashboard. However, the company also has a Row Access Policy applied to the 'SALES' table that restricts access based on the 'REGION' column, only allowing users to see data from their assigned region. The 'ANALYST ROLE is NOT assigned any specific region. Which approach will allow the dashboard to display the sales data and masked phone numbers for all regions without violating security policies?

  • A. Create a view that joins the 'SALES' and 'CUSTOMERS' tables, granting the 'ANALYST_ROLE 'SELECT privilege on the view. The view will inherit the Row Access Policy of the 'SALES' table, preventing the analyst from seeing all regions. The dynamic masking policy on 'CUSTOMER PHONE will still be applied.
  • B. Create a stored procedure that executes with 'OWNER rights. Inside the stored procedure, query the 'SALES' and 'CUSTOMERS' tables. Grant the 'ANALYST ROLE execute privilege on the stored procedure. This bypasses both the Row Access Policy and dynamic data masking and displays all data without any restrictions.
  • C. Create a secure view that joins the 'SALES' and 'CUSTOMERS' tables, granting the 'ANALYST_ROLE 'SELECT privilege on the view. The secure view preserves the Row Access Policy and dynamic masking, ensuring data security and role-based access.
  • D. Create a stored procedure that executes with 'CALLER rights. Inside the stored procedure, query the 'SALES and 'CUSTOMERS' tables. Grant the execute privilege on the stored procedure. This bypasses the Row Access Policy and displays all data while still applying dynamic data masking.
  • E. Create a view that joins the 'SALES' and 'CUSTOMERS' tables. Grant the 'ANALYST_ROLES 'SELECT privilege on the view. Remove the Row Access Policy from the 'SALES' table. The dynamic masking policy will still be applied.

Answer: C

Explanation:
A secure view is the best solution. It encapsulates the data access logic and respects both the Row Access Policy (limiting regional access) and Dynamic Data Masking (masking sensitive phone number data). Option B bypasses Row Access policies which isn't desirable. Option D is wrong as removing the Row Access Policy is generally a bad security practice. Option E bypasses the Row Access Policy and masking which is also undesirable.


NEW QUESTION # 188
You have a table named 'event_data' that tracks user activities. The table contains 'event_id' (INT), 'user _ id' (INT), (TIMESTAMP NTZ), 'event_type' (VARCHAR), and 'event_details' (VARIANT). The table is partitioned by Performance on queries filtering by both 'event_type' and a specific date range on is slow You suspect inefficient partition pruning and JSON parsing as potential bottlenecks. Which combination of actions will most effectively address these performance issues?

  • A. Change the partition key to 'event_type' and create a table function to query sevent_detailss.
  • B. Create a materialized view partitioned by and clustered by 'event_type' , pre-extracting relevant fields from 'event_detailS into separate columns.
  • C. Create a temporary table containing the results and then performing a Merge operation.
  • D. Create a view that extracts specific fields from the 'event_details' column into separate columns and add a secondary index on 'event_type' .
  • E. Add a masking policy on the 'event_details' column and recluster the table by 'user_id'.

Answer: B

Explanation:
Option B provides the most effective solution. Creating a materialized view addresses both problems: Partitioning by ensures efficient partition pruning when querying by date ranges. Clustering by 'event_type' improves performance when filtering on this column. Pre-extracting fields from sevent_detailS into separate columns avoids expensive JSON parsing at query time. Option A, adding index will not perform better than partition pruning. Option C changing partition key will require full reload of data and clustering table is expensive. Option D, masking policy will secure sensitive data but won't resolve performance issues. Option E, creating a temporary table and performing a Merge operation will increase cost and time.


NEW QUESTION # 189
You observe a 'Spilling to Local Storage' event in the Query Profile of a complex aggregation query. This indicates that the intermediate results are exceeding the memory capacity of the virtual warehouse. Which of the following actions would be MOST effective in mitigating this issue and improving the query performance?

  • A. Increase the value of the 'MAX MEMORY USAGE parameter for the session.
  • B. Optimize the query to reduce the amount of data processed before the aggregation.
  • C. Increase the virtual warehouse size to provide more memory resources.
  • D. Reduce the number of distinct values in the grouping columns.
  • E. Implement result caching to avoid re-computation.

Answer: B,C,D

Explanation:
Spilling to local storage indicates memory pressure. Increasing the virtual warehouse size provides more memory. Reducing the number of distinct values in grouping columns reduces the size of intermediate aggregation results. Optimizing the query to reduce data volume before aggregation also reduces memory footprint. ' is not a user-configurable parameter in Snowflake. Result caching won't help with the memory issue during the initial execution.


NEW QUESTION # 190
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

  • A. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
  • B. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.
  • C. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
  • D. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
  • E. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.

Answer: B,D

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.


NEW QUESTION # 191
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?

  • A. Create a calculated field in Snowsight that extracts the hour from the 'visit_timestamp' and then group by that calculated field to calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
  • B. Use the visit_timestamp)' function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter directly in the SQL query.
  • C. Use the function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
  • D. Use the 'TO_CHAR(visit_timestamp, 'YYYY-MM-DD HH24')' function to group data by hour, but only use this option if 'visit_timestamp' is already stored as TEXT.
  • E. Create a view that pre-aggregates the hourly visitor counts and includes the country. The dashboard queries this view and applies no additional filters.

Answer: B,E

Explanation:
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.


NEW QUESTION # 192
......

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