Prepare with Snowflake SPS-C01 exam practice material, pass for sure

Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowflake SQL statement intended to modify the properties of a Snowpark-optimized virtual warehouse named
'SNOWPARK_WH':

Which of the following statements accurately describe the expected outcome of executing this SQL statement?

A) The SQL statement will execute successfully only if the user executing it has the 'MODIFY privilege on the 'SNOWPARK_WH' warehouse.
B) The SQL statement will execute successfully after checking if 'SNOWPARK_WH' is of Snowpark-optimized warehouse type, resizing the 'SNOWPARK_WH' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
C) The SQL statement will fail because the 'SCALING POLICY parameter cannot be set for Snowpark-optimized warehouses.
D) The SQL statement will execute successfully, resizing the 'SNOWPARK WI-i' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
E) The SQL statement will fail because you cannot modify 'WAREHOUSE_SIZE and 'MAX CLUSTER COUNT in a single SALTER WAREHOUSE statement.

2. You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A) Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.
B) Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.
C) Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.
D) Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.
E) Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.

3. Consider the following Snowpark Python code snippet:

A) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
B) This code will ovemrite the table if it already exists.
C) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
D) This code will fail because is not a valid method for Snowpark DataFrames.
E)

4. Consider the following Snowpark Python code snippet that defines and applies a UDF:

Which of the following modifications would MOST likely improve the performance of this code, assuming the DataFrame 'df contains a large number of rows?

A) Specify a different warehouse size when creating the Snowpark session using 'session = Session.builder.config('warehouse', 'XLARGE').configs(connection_params).create()'.
B) Rewrite the 'apply_discount' function to use NumPy arrays internally for vectorized calculations, ensuring compatibility with vectorized UDF execution. The function signature will also need to accept arrays.
C) Remove the 'input_types' argument from 'session.udf.register' . Snowflake can automatically infer the input types.
D) Change to in the "session.udf.register' call, ensuring the function is updated to handle batches of data.
E) Use and F.lit(0.2Y instead of 0.1 and 0.2 while creating the dataframe.

5. You are tasked with processing a Snowpark DataFrame named 'orders df that contains order information. The DataFrame includes the following columns: 'order _ id' (INTEGER), 'customer_id' (INTEGER), 'order_date' (DATE), 'order_total' (STRING), and 'discount_code' (STRING). The 'order_total' column contains values with leading dollar signs and commas (e.g., '$1 ,234.56'). The column can contain codes like 'SAVEIO', 'SAVE20', or be NULL. Your goal is to create a new DataFrame 'transformed_df that includes the following transformations: 1 . Convert the 'order_total' column to a numeric value (DOUBLE) after removing the dollar signs and commas. 2. Apply a discount based on the 'discount_code'. If the 'discount_code' is 'SAVEIO', apply a 10% discount; if it's 'SAVE20', apply a 20% discount. If the 'discount_code' is NULL or any other value, apply no discount (0%). 3. Calculate the 'final_total' after applying the discount. Which of the following code snippets correctly and efficiently implements these transformations using Snowpark?

A)

B)

C)

D)

E)

Solutions: