2025 VALID DAA-C01 EXAM QUESTIONS | RELIABLE SNOWPRO ADVANCED: DATA ANALYST CERTIFICATION EXAM 100% FREE RELIABLE EXAM PASS4SURE

2025 Valid DAA-C01 Exam Questions | Reliable SnowPro Advanced: Data Analyst Certification Exam 100% Free Reliable Exam Pass4sure

2025 Valid DAA-C01 Exam Questions | Reliable SnowPro Advanced: Data Analyst Certification Exam 100% Free Reliable Exam Pass4sure

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q292-Q297):

NEW QUESTION # 292
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.

  • A. Create a materialized view from the 'calculate metrics CTE.
  • B. Reduce the 'MAX MEMORY USAGE IN parameter at the session level.
  • C. Rewrite the CTE to use smaller intermediate tables.
  • D. Partition the underlying tables used in the CTE.
  • E. Increase the warehouse size.

Answer: C,E

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 # 293
A Snowflake table 'USER ACTIVITY' contains a column 'ACTIVITY TIMESTAMP' of type VARCHAR, which stores timestamps in different formats, including ISO 8601 (e.g., '2023-10-27 Unix epoch seconds (e.g., '1698400800'), and milliseconds (e.g., '1698400800000'). You need to convert all these timestamps into a consistent TIMESTAMP NTZ data type in Snowflake. Which approach offers the most efficient and accurate solution for converting these diverse timestamp formats into a unified TIMESTAMP NTZ?

  • A. Use a UDF (User-Defined Function) that intelligently detects the timestamp format and applies the appropriate conversion function CTO TIMESTAMP , etc.).
  • B. Use a series of 'CASE' statements with ' TO_TIMESTAMP' and multiple format strings to parse each format individually.
  • C. First convert epoch milliseconds to epoch seconds by dividing by 1000. Then use a single 'TO TIMESTAMP' function assuming default ISO 8601 format.
  • D. Utilize Snowflake's automatic type conversion capabilities by simply changing the column's data type to TIMESTAMP_NTZ using ALTER TABL
  • E. Create separate temporary tables for each timestamp format using 'TO_TIMESTAMP' , then UNION ALL them together.

Answer: A

Explanation:
Option C provides the most efficient and accurate solution. While 'CASE' statements (Option A) are viable, they can become complex and less maintainable with many formats. Creating temporary tables (Option B) is inefficient. Dividing milliseconds and assuming ISO 8601 (Option D) will fail for true ISO 8601 formats and might cause loss of precision. Snowflake's automatic type conversion (Option E) won't handle the mixed formats. A UDF (Option C) encapsulates the logic for format detection and conversion, promoting code reusability and maintainability. It allows you to use different Snowflake functions within the UDF to parse different formats, ensuring accurate conversion to a unified TIMESTAMP NTZ data type.


NEW QUESTION # 294
A data analyst needs to forecast sales for the next quarter based on historical sales data'. The data is stored in a table named 'SALES DATA' with columns 'SALE DATE (DATE) and 'SALES_AMOUNT (NUMBER). The analyst wants to use the 'HOLT WINTERS function for forecasting, considering both trend and seasonality. However, the data contains some missing values for a few 'SALE DATE entries. Which of the following approaches is the MOST appropriate to handle missing data points while ensuring accurate forecasting with 'HOLT WINTERS in Snowflake?

  • A. Replace the missing ' SALES_AMOUNT values with the average sales amount calculated over the entire historical period before applying the HOLT WINTERS' function.
  • B. Interpolate the missing 'SALES_AMOUNT' values using linear interpolation based on the adjacent data points before feeding the data into the HOLT WINTERS' function.
  • C. Exclude the rows with missing ' SALES_AMOUNT' values from the dataset before applying the 'HOLT_WINTERS' function. This ensures only complete data points are used for the forecast.
  • D. Directly apply the 'HOLT WINTERS function to the data. The function will automatically handle the missing values by ignoring those dates.
  • E. Use the or function to impute the missing 'SALES_AMOUNT values based on neighboring dates before applying the HOLT WINTERS' function.

Answer: B

Explanation:
HOLT_WINTERS function requires continuous data for time series analysis, and missing values will lead to inaccurate forecasts. Option E is the most appropriate because Interpolation method such as linear interpolation will give you more precise result. Other approaches like direct use or Exclusion will result in wrong forecasting results.


NEW QUESTION # 295
You are loading data from a series of CSV files into Snowflake using Snowsight. The files have varying column orders and some files are missing certain columns. You need to ensure that all data is loaded into a consistent table schema, handling missing columns gracefully.
Which of the following strategies is MOST effective in Snowsight to achieve this?

  • A. Create multiple file formats, one for each unique CSV file structure. Use Snowsight's 'Load Data' wizard to load each set of files with the corresponding file format. Use UNION ALL to combine the data from multiple tables into a single view.
  • B. Define a file format with 'SKIP HEADER = 1', "FIELD OPTIONALLY ENCLOSED BY = and "NULL _ IF = (", 'NULL')'. Create a single table with all columns defined and use Snowsight's 'Load Data' wizard to load the CSV files. Columns not present in a given CSV file will automatically be populated with NULL.
  • C. Load the data into a staging table with a single VARIANT column. Then, use SQL with 'CASE statements and 'GET ' function to extract data from the VARIANT column into the target table with the desired schema.
  • D. Define a file format with 'SKIP_HEADER = 1 ' and load all CSV files into a single table with all possible columns defined as VARCHAR. After loading, use SQL queries with and to convert the data to the appropriate data types.
  • E. Pre-process the CSV files before loading using a scripting language (e.g., Python) to standardize the column order and add missing columns with NULL values. Then, load the pre-processed files into Snowflake using Snowsight.

Answer: B

Explanation:
Option D is the most effective strategy. By defining the file format with 'SKIP_HEADER = , FIELD_OPTIONALLY_ENCLOSED_BY ' and 'NULL_IF = (", 'NULL')', Snowflake can handle missing columns by populating them with NULL values during the load process. Creating a single table with all columns defined ensures data consistency. Option A works, but the type conversion after loading is less efficient and more error-prone. Option B requires managing multiple file formats and using UNION ALL, which can be complex. Option C using VARIANT will work, but adds extra complexity. Option E requires an external preprocessing step, which is less desirable.


NEW QUESTION # 296
You are tasked with creating a data access strategy for a marketing analytics team. They need access to customer purchase data, but only aggregated by region and product category. They should not be able to see individual customer details due to PII compliance. You decide to use a Secure View. Which of the following are the MOST appropriate steps to ensure data security and minimize performance impact?

  • A. Create a Secure View that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
  • B. Create a Materialized View that aggregates the data. Create a Secure View on top of the Materialized View and grant SELECT privilege on the secure view to the marketing analytics role.
  • C. Create a regular view that aggregates the data and grant SELECT privilege on the view to the marketing analytics role.
  • D. Create a Secure View directly on the base tables with the aggregation logic. Grant SELECT privilege on the view to the marketing analytics role.
  • E. Create a Materialized View directly on the base tables with the aggregation logic. Grant SELECT privilege on the Materialized View to the marketing analytics role.

Answer: A

Explanation:
Secure Views prevent access to the underlying tables and data lineage. Creating the Secure View directly with the aggregation prevents unauthorized access to detailed data and avoids exposing intermediate tables or views. Materialized views while improving performance, when used along with a Secure View adds unnecessary complexity as aggregation should happen in secure view only.


NEW QUESTION # 297
......

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