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Snowflake SPS-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance Optimization and Best Practices	20%	- Warehouse sizing for Snowpark - Caching strategies - Query pushdown and optimization - Debugging and explain plans - Vectorized UDFs - Minimizing data transfer
Topic 2: Snowpark Concepts	15%	- Transformations vs. Actions - Stored procedures and conditional logic - Snowpark DataFrames and query plans - Client-side vs. Server-side execution - Snowpark Sessions and connection management - Snowpark architecture and core concepts
Topic 3: Snowpark API for Python	30%	- Reading and writing data - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management - Working with Semi-structured data
Topic 4: Data Transformations and DataFrame Operations	35%	- Filtering, Aggregating, and Joining DataFrames - Using built-in functions - Window functions - Persisting transformed data - Complex data pipelines

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q131-Q136):

NEW QUESTION # 131
You have a Snowpark DataFrame named 'orders_df with columns 'order_id', 'customer_id', 'order_date', and 'order_total'. You need to perform the following data enrichment steps using Snowpark for Python: 1. Calculate the 'year' from the 'order_date' column. 2. Calculate the 'discounted_total' by applying a discount of 10% if the 'order_total' is greater than $100, otherwise, no discount. 3. Create a new column 'customer_tier' based on the total spend per customer for each year. Customers with total spend greater than $1000 are 'Gold', between $500 and $1000 are 'Silver', and below $500 are 'Bronze'. Which of the following code snippets correctly implements these data enrichment steps using Snowpark (Assume the existence of a customer total spend df DataFrame).

Answer: D

Explanation:
Option B is the most efficient and correct. It calculates 'year' and 'discounted_total' using built-in functions. It then groups by 'customer_id' and 'year' to calculate 'total_spend'. Critically, it then assigns the 'customer_tier' using a series of 'when' statements directly within Snowpark, avoiding the performance overhead of a UDE Finally, it joins the customer tier information back to the original 'orders df. Option A implements Customer Tier calculation using UDF, Option C introduces Windowing without need. Options D, E are incomplete.

NEW QUESTION # 132
You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

A. Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
B. Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.
C. Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
D. Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.
E. Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.

Answer: B,C,D

Explanation:
'DataFrame.repartition(V allows you to redistribute data based on a chosen key, improving locality for operations that depend on that key. Snowflake's automatic clustering physically organizes data on disk, improving read performance. Combining with repartition()' ensures that frequently accessed, well-partitioned data is readily available in memory close to the processing nodes, boosting performance. Disabling the cache makes the application's performance worse. The warehouse size, if not large enough, can also lead to data spilling to disk, decreasing the performance.

NEW QUESTION # 133
You are tasked with operationalizing a Snowpark Python UDF for batch scoring of a large dataset. The UDF takes a set of feature columns and returns a prediction. You want to optimize performance and resource utilization. Select all the strategies that would effectively improve the operational efficiency and scalability of your UDF execution.

A. Ensure that the Snowpark DataFrame being passed to the UDF is appropriately partitioned based on a relevant column (e.g., a geographical region) before invoking the UDF.
B. Always use a warehouse size of 'X-Large' or larger regardless of the data volume to guarantee sufficient resources for UDF execution.
C. If the UDF performs external API calls, implement retry logic with exponential backoff to handle transient network errors gracefully.
D. Adjust the 'MAX BATCH SIZE parameter for the warehouse executing the UDF to the largest possible value to minimize overhead.
E. Utilize the 'vectorized' argument during UDF registration to enable batch processing of input data within the UDF.

Answer: A,C,E

Explanation:
Partitioning the input DataFrame (A) allows Snowflake to distribute the UDF execution across multiple nodes, improving parallelism. The 'vectorized' argument (B) enables the UDF to process data in batches, reducing per-row overhead. Implementing retry logic (D) improves resilience when calling external APIs. is not configurable. Using a fixed 'X-Large' warehouse (E) is not cost-effective; right- sizing the warehouse based on workload is crucial.

NEW QUESTION # 134
Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

A. The UDF is registered as a temporary UDF and will be removed when the session ends.
B. The 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
C. The UDF is registered as a permanent UDF and stored in the specified stage for future use.
D. The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.
E. The default value of 'salutation' in the Python function will be used even when calling the UDF from SQL if the salutation parameter is omitted.

Answer: B,C,E

Explanation:
The correct answers are C, D, and E. makes the UDF permanent. 'replace=True' overwrites any existing UDF with the same name. Python's default parameter value IS used in the SQL call if the salutation is omitted. 'input_typeS are not redundant, they are required and Python's type hints are not automatically used. Option A is incorrect because 'is_permanent' is set to true.

NEW QUESTION # 135
You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A. Store the API key as an environment variable within the Snowflake warehouse configuration.
B. Store the API key in a Snowflake table and query it within the stored procedure.
C. Store the API key as a constant string within the stored procedure's code.
D. Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
E. Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.

Answer: E

Explanation:
Option D is the MOST secure and recommended approach. Snowflake Secrets provide a secure way to store sensitive information like API keys. They are encrypted and managed by Snowflake, reducing the risk of unauthorized access. The 'secrets' module allows you to retrieve these secrets within your stored procedures without exposing them in the code. Option A is the least secure because the API key would be directly visible in the stored procedure's code. Option B is slightly better than A, but still poses a risk of exposure if the table is compromised. Option C is not a valid option as you cannot define environment variables within the Snowflake warehouse configuration. Option E is the worst option as anyone with access to view the stored procedure definition can readily see the API Key. Further, Comments can be accidentally printed in logs.

NEW QUESTION # 136
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