Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
B) Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
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.

2. A Snowpark application processes streaming data from Kafka, performing complex windowing aggregations. The application is configured with auto-scaling enabled for the virtual warehouse. During peak hours, the application exhibits high latency despite the warehouse scaling up. Upon investigation, you observe sustained high CPU utilization on the single active warehouse. Which actions, alone or in combination, would MOST effectively improve performance while minimizing cost?

A) Increase the MIN_CLUSTER_COUNT parameter to pre-warm additional clusters. This ensures that clusters are readily available when the workload increases, reducing latency.
B) Decrease the SCALING_POLICY parameter to reduce the time it takes for warehouses to autoscale. This will allow warehouses to keep up with processing as volume increases.
C) Increase the MAX CLUSTER COUNT parameter for the virtual warehouse. This ensures that the warehouse can scale out to a greater number of clusters to handle the increased workload.
D) Repartition the input data to distribute the workload more evenly across the available clusters. Ensure the partitioning key is suitable for the aggregations being performed.
E) Optimize the Snowpark code by using vectorization and efficient data structures. This reduces the CPU load for each processing task.

3. You are tasked with setting up secure authentication for your Snowpark application. You want to use key pair authentication for a service user. Which of the following steps are necessary and in the correct order?

A) 1. Generate an RSA key pair (private and public key). 2. Store the private key securely on the client machine. 3. Associate the private key with the Snowflake user using the SALTER USER command. 4. Provide the public key in the Snowpark session configuration.
B) 1. Generate an RSA key pair (private and public key). 2. Store the private key securely on the client machine. 3. Provide the path to the private key file and passphrase (if any) in the Snowpark session configuration. 4. Associate the public key with the Snowflake user using the 'ALTER USER command.
C) 1. Generate an RSA key pair (private and public key). 2. Store the public key securely on the client machine. 3. Provide the path to the public key file in the Snowpark session configuration. 4. Associate the private key with the Snowflake user using the SALTER USER command.
D) 1. Generate an RSA key pair (private and public key). 2. Store the private key securely on the client machine. 3. Provide the path to the private key file in the
E) 1. Generate an RSA key pair (private and public key). 2. Store the private key in a database table. 3. Use database credentials in the Snowpark session configuration. 4. Associate the public key with the Snowflake user using the 'ALTER USER command.

4. 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)

5. You are tasked with creating a UDTF using Snowpark Python that splits a comma-separated string of customer IDs into individual rows. The input is a string (VARCHAR) and the output should be a table with a single column named 'customer_id' of type INTEGER. Which of the following code snippets CORRECTLY defines and registers the UDTF, ensuring proper data type handling? Assume is a valid Snowpark Session object.

A)

B)

C)

D)

E)

Solutions: