LATEST DAA-C01 TEST COST | LATEST DAA-C01 TEST VOUCHER

Latest DAA-C01 Test Cost | Latest DAA-C01 Test Voucher

Latest DAA-C01 Test Cost | Latest DAA-C01 Test Voucher

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
A data analyst is implementing a data preparation pipeline using Snowflake stored procedures to cleanse and transform data,. During testing, the analyst encounters unexpected errors within the stored procedures. Which strategies should the analyst employ to effectively debug and troubleshoot these stored procedures within Snowflake?

  • A. Rely solely on the error messages returned by Snowflake when the stored procedure fails.
  • B. Debug by trial and error, modifying the stored procedure code and re-executing until the error is resolved.
  • C.
  • D. Implement a custom error logging mechanism within the stored procedure using 'SYSTEM$LOG' to capture error messages and write them to a dedicated logging table.
  • E. Use the 'GET function on the stored procedure to review the code for potential errors.

Answer: C,D

Explanation:
Option A describes the correct method for obtaining the query ID of the failing procedure and querying the query history for detailed error information. Option B is also correct, implementing detailed error handling within the stored procedure provides greater insight into where the procedure fails. Option C is insufficient, Snowflake's general error messages are not always descriptive. Option D does not help during debugging and troubleshooting a running stored procedure. Option E is an inefficient and unreliable debugging approach.


NEW QUESTION # 28
Your company uses Snowflake to store sales data'. A dashboard reporting weekly sales trends is performing poorly. The underlying table, 'SALES DATA, contains billions of rows with columns like 'SALE DATE, 'PRODUCT ID', 'CUSTOMER D', and 'SALE AMOUNT. The dashboard queries use 'SALE DATE for filtering and grouping. The query execution plan shows full table scans. You need to optimize the dashboard's performance with minimal impact on data loading processes. Which of the following strategies should you implement FIRST to improve query performance?

  • A. Create a clustered table on 'SALE DATE' in the 'SALES DATA' table.
  • B. Partition the 'SALES_DATX table by 'SALE_DAT
  • C. Create a search optimization on 'SALES DATA' table for 'SALE DATE.
  • D. Create a materialized view that aggregates sales data weekly by 'PRODUCT_ID and 'CUSTOMER ID
  • E. Increase the warehouse size to X-LARGE.

Answer: A

Explanation:
Clustering the table on 'SALE DATE' is the most effective initial strategy. It physically organizes the data based on 'SALE DATE, which the dashboard queries use for filtering, thus reducing the amount of data scanned during query execution. Materialized views require ongoing maintenance and may not be the most efficient starting point. Increasing the warehouse size will increase the resource, but doesn't solve the underlying problem of full table scans, and search optimization is less efficient than clustering for date-based filtering. Snowflake does not support user-defined partitioning. Hence option A is the most appropriate choice.


NEW QUESTION # 29
You are analyzing website traffic data in Snowflake. The 'web_events' table contains 'event_timestamp' (TIMESTAMP N T Z), 'user_id', and 'page_url'. You discover that many 'event_timestamp' values are significantly skewed towards the future (e.g., a year ahead), likely due to incorrect device clocks. You want to correct these skewed timestamps by assuming the majority of events are valid and calculating a time drift. Which of the following strategies using Snowflake functionality would be MOST efficient and accurate for correcting these timestamps?

  • A. Calculate the average 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the average. Subtract this difference from the future skewed events to correct them.
  • B. Calculate the median 'event_timestamp' for each 'user_id' and subtract the overall median 'event_timestamp' from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
  • C. Calculate the average 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
  • D. Calculate the mode of the 'event_timestamp' and subtract it from each individual timestamp to derive a 'time_drift'. Then, subtract the 'time_drift' from each 'event_timestamp'.
  • E. Calculate the median 'event_timestamp' of all events. Then, for each 'event_timestamp', calculate the difference between the individual timestamp and the median. Subtract this difference from the future skewed events to correct them.

Answer: E

Explanation:
Option D provides the most robust approach. Using the median minimizes the impact of outliers (future-dated timestamps). Calculating the difference between each event timestamp and the overall median timestamp isolates the 'time_drift' for each record, which is then subtracted from each future skewed events. Option A uses median for each user, which is unneccesary. Options B and E are vulnerable to outliers (the very problem we're trying to solve). Option C, while conceptually interesting, isn't directly supported as a native aggregate function for timestamps in most SQL dialects, including Snowflake, without custom user-defined functions (UDFs), making it less efficient and potentially less accurate.


NEW QUESTION # 30
You are tasked with enriching your company's customer transaction data with external economic indicators (e.g., unemployment rate, GDP) obtained from a Snowflake Marketplace data provider. The transaction data resides in a table 'TRANSACTIONS' with columns 'TRANSACTION (INT), 'TRANSACTION DATE (DATE), and (VARCHAR). The economic indicators data, obtained from the Marketplace, is available in a table 'ECONOMIC DATA' with columns 'DATE (DATE), ZIP_CODE (VARCHAR), 'UNEMPLOYMENT RATE (NUMBER), and 'GDP' (NUMBER). Due to data quality issues, some zip codes in both tables are missing or malformed. You need to create a view that efficiently joins these two tables, handles missing or malformed zip codes, and provides the transaction data enriched with the economic indicators. Which of the following approaches is the MOST robust and efficient way to create this enriched view, minimizing data loss and maximizing data quality?

  • A. Create a view using a 'LEFT OUTER JOIN' between 'TRANSACTIONS and ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION_DATE =ECONOMIC_DATA.DATE' and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE'. Additionally, use the function to handle malformed zip codes and the 'NVL' function to replace missing or malformed zip codes with a default zip code (e.g., '00000') for joining purposes. Also include a new column "ENRICHMENT SUCCESS' that flag indicates that the join was successful or whether data was enriched using the default zip code.
  • B. Create a stored procedure that iterates through each transaction in 'TRANSACTIONS' , attempts to find a matching economic data record in ECONOMIC_DATA' based on date and zip code, and updates a new 'TRANSACTIONS_ENRICHED table with the economic indicators. Handles missing zipcodes by setting the 'UNEMPLOYMENT RATE' and 'GDP ' to 0 for any record in transaction which zip code is missing.
  • C. Create a Snowflake Task that runs daily to update a materialized view that joins 'TRANSACTIONS' and 'ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE , handling missing zip codes by skipping those records entirely.
  • D. Create a view that first filters out all rows with missing or malformed zip codes from both 'TRANSACTIONS' and 'ECONOMIC DATA' using 'WHERE clauses and regular expressions to validate the zip code format. Then, perform an ' INNER JOIN' between the filtered datasets on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE.
  • E. Create a view that performs a simple 'JOIN' between 'TRANSACTIONS' and 'ECONOMIC DATA' on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC_DATDATE and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE. This approach ignores missing or malformed zip codes.

Answer: A

Explanation:
Option C provides the most robust and efficient solution. Using 'LEFT OUTER JOIN' ensures that all transactions are included in the view, even if there is no matching economic data. 'TRY TO NUMBER handles malformed zip codes gracefully by converting valid zip codes to numbers and returning NULL for invalid ones, preventing errors. 'NVL' replaces NULL zip codes (either originally missing or resulting from TRY_TO_NUMBER) with a default value, allowing the join to proceed using a fallback. Adding the 'ENRICHMENT_SUCCESS' flag provides transparency about which records were enriched using the default zip code, enabling users to assess the reliability of the enriched data. Option A is inadequate because it ignores missing or malformed zip codes, leading to data loss. Option B is inefficient and not scalable due to row-by-row processing. Option D discards records with missing or malformed zip codes, resulting in significant data loss. Option E does not specifically handle data quality issues related to missing or malformed zip codes. Further the use of Tasks and materialized views, while increasing performance, doesn't necessarily address the issue of data quality.


NEW QUESTION # 31
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

  • A. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
  • B. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
  • C. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.
  • D. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.
  • E. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.

Answer: A,E

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.


NEW QUESTION # 32
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