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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A) Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.
B)

C)

D) Require all users to remove the password protection from their private keys to simplify the session creation process.
E) Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.

2. You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

A) Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
B) Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.
C) Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
D) Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
E) Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.

3. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A)

B)

C)

D)

E)

4. You are using Snowpark Python to build a machine learning pipeline. One step in the pipeline involves feature engineering using a large dataset. This feature engineering step is computationally expensive and involves several transformations. You want to optimize the performance of this step by caching intermediate results. Given the following code snippet, which of the following strategies would be MOST effective for optimizing the performance, considering the use of

A) Cache each intermediate DataFrame after each individual transformation step, even if the DataFrame is only used once.
B) Avoid using altogether because it can introduce overhead and is not always beneficial.
C) Identify DataFrames that are reused multiple times and cache them using after the transformations that generate them.
D) Cache the final DataFrame only after all feature engineering steps are completed.
E) Cache the initial raw data DataFrame before applying any transformations.

5. You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

A)

B)

C)

D)

E)

Solutions: