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NEW QUESTION # 157
You are working with a Snowflake table called containing sales transactions. The table includes columns like 'transaction_id' (VARCHAR), 'product_id' (VARCHAR), 'transaction_date' (DATE), (NUMBER(10,2)), and (GEOGRAPHY). You need to perform the following data preparation tasks: 1. Clean the data: Remove transactions with less than or equal to 0. 2. Transform the data: Convert the 'customer_location' from GEOGRAPHY to a WKT string representation. 3. Enrich the data: Calculate the year and month from the 'transaction_date' and add them as new columns. 4. Mask the data: Partially mask the 'transaction_id' column to protect customer data by only displaying the last 4 digits of the ID. Considering performance and best practices, what is the MOST efficient way to achieve these transformations using Snowflake features? You need to achieve this in a single statement.
Answer: A,E
Explanation:
Options A and D are functionally equivalent and correct, they both performs the desired data cleansing, transformation, and enrichment in a single 'CREATE OR REPLACE TABLE' statement. The 'WHERE sales_amount > clause filters out invalid transactions. RIGHT(transaction_id, 4) masks the transaction ID. YEAR() and MONTH() extract the year and month from the date, and ST_ASTEXT() converts the GEOGRAPHY object to a WKT string. Option B uses HASH, but the question asks for partially masking so HASH isn't suitable. Option C uses CASE, that is not required. Option E makes use of SYSTEM$MASK, function which masks the entire column value, that isn't what the questions asks.
NEW QUESTION # 158
You have a stored procedure written in Python within Snowflake that needs to process a large dataset. The procedure's performance is critical, and you want to optimize it for speed. Which of the following strategies would be MOST effective in improving the performance of your Python stored procedure when dealing with large datasets in Snowflake? (Select TWO)
Answer: B,D
Explanation:
Options A and C are the most effective strategies. Option A, 'execute_stream' , is ideal for large datasets as it avoids loading everything into memory. Option C, leveraging Pandas DataFrames for vectorized operations, significantly speeds up data manipulation within Python. Option B is partially correct. SQL is often faster, but rewriting a complex Python procedure in SQL can be time-consuming and may not always be feasible or optimal. Option D, increasing the warehouse size, can help, but it's more of a general performance improvement rather than a specific optimization for Python stored procedures. Also it will cost more. E is incorrect as recursive SQL queries, while sometimes useful, aren't a general-purpose replacement for Python loops and can be less efficient for complex logic.
NEW QUESTION # 159
You're using Snowsight to build a dashboard for monitoring website performance. The data is in a table called 'WEB EVENTS' with columns: 'EVENT _ TIME' (TIMESTAMP_NTZ), 'EVENT _ TYPE' (VARCHAR, e.g., 'page_view', 'button_click'), 'USER_ID' (VARCHAR), and 'PAGE URL' (VARCHAR). You want to create a tile that shows the average time between consecutive 'page_view' events for each user over the last 7 days. This will help you understand how users are navigating the site. Assume that for a single user, page_view events are ordered by EVENT TIME. Which of the following SQL queries, when used as the basis for a Snowsight tile, will correctly calculate this average time difference in seconds?
Answer: D
Explanation:
It uses the window function to get the previous event time for each user, then calculates the difference between consecutive event times in seconds using 'TIMESTAMP_DIFF. The outer query then averages these differences for each user. The 'WHERE PREVIOUS_EVENT_TIME IS NOT NULL' clause is important to exclude the first event for each user, which would have a null previous event time. Option B attempts to subtract timestamps directly, which is not the correct way to get the difference in seconds in Snowflake. Option C uses 'DATEDIFF which has the parameters in the wrong order compared to the logic of the question. Option D incorrectly uses FIRST _ VALUE. Option E omits the subquery necessary to correctly use the LAG function.
NEW QUESTION # 160
In what ways do stored procedures differ from user-defined functions (UDFs) in SQL?
Answer: B
Explanation:
Stored procedures and UDFs differ in their ability to execute repetitive tasks.
NEW QUESTION # 161
Your company needs to comply with GDPR regulations, and you're tasked with implementing data masking to protect sensitive customer information. Specifically, you need to mask email addresses in a 'CUSTOMERS' table during data preparation. Which of the following approaches, utilizing Snowflake's system functions and data masking capabilities, offers the MOST secure and flexible solution, while allowing authorized users to potentially unmask the data if necessary?
Answer: D
Explanation:
Option C provides the best combination of security and flexibility. Snowflake's masking policies, combined with functions like , allow for dynamic masking based on user roles. Authorized users (e.g., those in an 'analyst' role) can see the original email address, while others see a masked version. Options A and D are not recommended as hashing/encryption functions like SHA2() and MD5() are not easily reversible and are not the intended use case for data masking policies. Option B offers masking with REGEXP_REPLACE but no mechanism to unmask with proper permissions. Option E creates a view, which is not centrally managed or scalable solution.
NEW QUESTION # 162
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