Reliable SPS-C01 Test Notes & Real SPS-C01 Questions

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Actual Snowflake SPS-C01 Exam Questions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q234-Q239):

NEW QUESTION # 234
You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

Answer: B

Explanation:
Option C is the most efficient and accurate. It directly compares the 'registration_date' (TIMESTAMP_NTZ) to the date range using string literals, avoiding unnecessary function calls Cyear', 'to_date', 'to_varchar', that could impact performance or introduce subtle errors related to timezone conversions. Since TIMESTAMP_NTZ has no timezone, direct comparison is safe and optimal. Options A and E, while seemingly straightforward, involve function calls for each row, which can be slower. Option B uses 'like' on a date converted to string, which is less efficient and can be problematic with different date formats. Option D converts the date to a VARCHAR, which is unnecessary and impacts performance.


NEW QUESTION # 235
A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

Answer: A

Explanation:
Specifying a warehouse size and using auto-suspend and auto-resume provides a balance between performance and cost. Option A might increase costs due to idle time. Option B relies on Snowflake's default warehouse, which might not be optimal. Option D could increase overall execution time due to overhead. Option E may not be feasible or efficient if the logic is heavily dependent on Python libraries. Therefore, specifically assigning an appropriate warehouse size with auto-suspend/resume is the most effective approach.


NEW QUESTION # 236
You are building a Snowpark application that requires you to connect to Snowflake from an environment where directly specifying credentials in the code is not permitted for security reasons. Which of the following are valid and recommended ways to securely pass authentication information to the Snowpark Session?

Answer: A,B,E

Explanation:
Options A, C, and D represent valid ways to handle credentials securely. Environment variables (A) are a standard practice for configurations. Using a secret management service (C) provides the best security posture for production environments. Using the Snowflake CLI (D) is acceptable for development. Storing credentials in a Snowflake stage (B) adds unnecessary complexity and doesn't inherently improve security over other options. Base64 encoding (E) is not a secure method; it's easily decoded and provides a false sense of security. Hardcoding and obfuscating credentials is not recommended.


NEW QUESTION # 237
You have a Snowpark DataFrame named containing order data that needs to be inserted into the 'ORDERS table. However, due to a recent data ingestion issue, some records in might already exist in the 'ORDERS table based on the 'ORDER ID' column. Your goal is to insert only the new orders into the 'ORDERS table while avoiding duplicates. Which of the following approaches, combining efficiency and correctness, is most suitable for this task? Assume 'session' and required libraries are already imported.

Answer: A,D

Explanation:
Options A and C are both suitable and efficient. Option A uses a 'left_anti' join to identify records in 'staged_orders' that do not exist in the 'ORDERS' table based on 'ORDER ID. This is a standard and efficient way to filter out existing records using Snowpark's DataFrame operations. Option C suggests a stored procedure with a MERGE statement, which is highly efficient for upsert operations directly within Snowflake. Option B is inefficient because it collects all the order IDs from the 'ORDERS table into the driver's memory, which could cause memory issues with large datasets. Option D is incorrect as 'on_duplicate_key' is not a valid parameter for insert_into method. Option E is using pandas dataframe to insert, which might not perform well in terms of scale.


NEW QUESTION # 238
You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?

Answer: C

Explanation:
The 'session.call()' method (B) is the most direct and efficient way to call a stored procedure and retrieve its single return value from Snowpark Python. It handles the execution and data retrieval efficiently. Using 'session.sql()' (A and C) is less efficient as it requires parsing SQL and manual extraction of the result. 'session.sproc()' (D) is generally used to register Python functions as stored procedures and not for calling existing ones. is not valid, 'session.call()' is the right way to call procedure


NEW QUESTION # 239
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