01. A Snowpark Specialist needs to define a Python function to be used as a stored procedure. What should they consider?
a) The function must always return a Snowpark DataFrame.
b) The first parameter for the function must be a Session class object.
c) The @sproc decorator must always be used before the function definition.
d) The pandas DataFrame or pandas Series object can be used as parameters for the function.
02. Which workload would benefit the MOST from using a Snowpark-optimized virtual warehouse?
a) Machine learning training
b) Machine learning inference
c) Registering a model into the Snowflake Model Registry
d) Creating a compute pool in Snowpark Container Services
03. A Snowpark Specialist wants to create a Python User-Defined Function (UDF) and operationalize it in Snowflake. The function will not use the IMPORTS clause. What can the Specialist do with this Python UDF in Snowflake?
a) Share the Python UDF directly.
b) Share a view that calls the Python UDF.
c) Access the session object within the Python UDF.
d) Grant the USAGE privilege on the Python UDF to a role.
04. What is the main difference between scaling up and scaling out a Snowpark warehouse?
a) Scaling up increases warehouse size, while scaling out adds clusters
b) Scaling up adds more nodes, while scaling out increases concurrency
c) Scaling up decreases warehouse credits, while scaling out increases them
d) Scaling up is automatic, while scaling out must be configured manually
05. How can a Snowpark Specialist summarize the sales quantity by product, given a DataFrame containing product sales quantities in columns named product_id and quantity?
a) df.sum("quantity").group_by("product_id")
b) df.summarize("quantity").over("product_id")
c) df.group_by("product_id").agg(sum("quantity"))
d) df.agg("quantity", type="sum").group_by("product_id")
06. What should be done if a Snowpark session fails to connect?
a) Check Snowflake account credentials and network settings
b) Manually increase the session timeout
c) Increase warehouse size to improve connectivity
d) Disable authentication methods
07. How can you use a third-party Python library inside a Snowpark UDF?
a) Manually install the package on Snowflake servers
b) Use the Anaconda repository to import the package
c) Run the package installation using pip inside Snowflake
d) Only built-in Python libraries are supported in UDFs
08. Which method retrieves the first few rows of a Snowpark DataFrame?
a) df.first()
b) df.take()
c) df.show()
d) df.fetch()
09. A Snowpark Specialist developed an application that uses Snowpark for Python to interact with Snowflake tables. Users are reporting constant Multi-Factor Authentication (MFA) alerts. What is the MOST secure method of reducing the MFA requests?
a) Create a NETWORK POLICY for the affected users.
b) Set the account parameter ALLOW_CLIENT_MFA_CACHING to TRUE.
c) Allow users to add a passcode as part of their Snowpark session creation.
d) Disable MFA temporarily for affected users using the parameter DISABLE_MFA.
10. Why are temporary tables useful for Snowpark applications?
a) They improve performance by reducing data re-processing
b) They automatically cache query results indefinitely
c) They can be queried across multiple sessions
d) They require fewer Snowflake credits than standard tables
We have prepared Snowflake SnowPro Specialty - Snowpark (SPS-C01) certification sample questions to make you aware of actual exam properties. This sample question set provides you with information about the SnowPro Specialty - Snowpark exam pattern, question formate, a difficulty level of questions and time required to answer each question. To get familiar with Snowflake Certified SnowPro Specialty - Snowpark exam, we suggest you try our Sample Snowflake SPS-C01 Certification Practice Exam in simulated Snowflake certification exam environment.