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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?

A)

B)

C)

D)

E)

2. You are working with two large Snowpark DataFrames: 'transaction_df and 'product df. 'transaction_df contains transaction data including 'transaction id', 'product id', and 'transaction_date'. 'product df contains product details including 'product id', product_name', and 'product category'. You need to join these DataFrames to analyze transaction data by product category. The 'transaction_df is significantly larger than 'product_df. Which of the following strategies can significantly improve the performance of the join operation in Snowpark? (Select all that apply)

A) Ensure that the 'product_id' column in both DataFrames is of the same data type and has statistics collected on it.
B) Use a 'hint' to force Snowflake to use a specific join algorithm like 'MERGE JOINS.
C) Filter the 'transaction_df to a smaller subset based on 'transaction_date' before performing the join, if only recent transactions are needed.
D) Use a broadcast join by explicitly specifying 'broadcast-True in the 'join' function when joining 'product_df to 'transaction_df.
E) Cache the 'transaction_df DataFrame before the join operation using

3. You've written a Snowpark stored procedure in Python that utilizes a custom Python library named 'my utils.py' containing utility functions for data transformation. This library is not available in Anaconda'. You want to deploy the stored procedure and ensure that the custom library is accessible during execution. Which is the most appropriate way to operationalize your Snowpark stored procedure with the custom library?

A) Create a Snowpark DataFrame from 'my_utils.py' and pass it as an argument to the stored procedure.
B) Upload 'my_utils.py' to a Snowflake stage and specify the stage path in the 'imports' argument of the 'session.sproc.register' method.
C) Install 'my_utils.py' on the Snowflake warehouse where the stored procedure will be executed.
D) Copy the contents of 'my_utils.py' directly into the stored procedure's code.
E) Package 'my_utils.py' into a wheel file and upload it to a Snowflake stage. Then, add the stage path to the 'packages' argument of the 'session.sproc.register' method.

4. 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) Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.
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) Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.

5. You have a Snowpark application that performs machine learning inference on a large dataset of images stored in Snowflake. The inference logic is implemented within a Python UDF that utilizes a pre-trained deep learning model. You notice that the inference process is slow and consumes a significant amount of resources. Which of the following optimization techniques would be MOST effective in improving the performance and reducing the resource consumption of this application?

A) Optimize the UDF code to use batch processing techniques to process multiple images in a single call, reducing the overhead of UDF invocation.
B) Use external functions instead of UDFs.
C) Ensure the virtual warehouse used by Snowpark is configured with auto-scaling to dynamically adjust the compute resources based on the workload.
D) Persist the pre-trained deep learning model in a database table or stage and load it into the UDF's memory during each invocation to avoid repeated loading.
E) Leverage the Snowpark Session object to manage the lifecycle and scope of the pre-trained model within the UDF, ensuring it is loaded only once per session.

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