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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)

A) Using pushdown optimization by writing UDFs in Scala and ensuring filter operations are applied as early as possible in the data processing pipeline.
B) Eagerly evaluating all DataFrame transformations using 'df.collect()' after each transformation to materialize the intermediate results.
C) Using on intermediate DataFrames that are reused multiple times in subsequent transformations.
D) Using to explicitly define the order in which DataFrames should be processed.
E) Leveraging Snowflake's caching mechanisms by using the 'CACHE RESULT clause after complex or frequently used queries.

2. You have a Snowpark application that processes sensitive data'. To enhance security, you want to use key pair authentication and ensure that the private key is never exposed in plain text within the application or logs. Which of the following strategies offers the most robust protection against accidental key exposure, even in the event of a security breach of the application server itself?

A) Store the private key in an environment variable on the application server, ensuring that only the application user has read access.
B) Use a dedicated Hardware Security Module (HSM) or a cloud-based key management service (e.g., AWS KMS, Azure Key Vault) to store and manage the private key. Configure the Snowpark application to retrieve the key only when needed and never store it locally.
C) Store the private key in an encrypted file with restricted access, decrypting it only during session creation and overwriting the memory location immediately afterwards.
D) Store the private key encrypted using AES encryption with a key derived from a passphrase. Prompt the user for the passphrase each time the application starts.
E) Store the private key encrypted with a symmetric key. Store the symmetric key in a separate environment variable.

3. Consider the following Snowpark Python code snippet:

A) The function will retrieve all rows from the 'customers' table and store them in a local Pandas DataFrame before applying the function.
B)

C) This code requires a configured Anaconda environment to run successfully.
D) The code demonstrates the Snowpark architecture, where transformations are translated into SQL and executed in Snowflake's engine. Only the final 'collect()' brings the results back to the client.
E) The 'upper()' function will be executed on the client-side (where the Python code is running) for each row in the 'customers' table.

4. A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?

A) Create a global session object and use 'session.use_warehouse(warehouse_name)' before each operation. This implicitly switches the warehouse for all operations on that session.
B) Create a single session and create multiple Snowpark DataFrames, specifying the warehouse in each DataFrame's 'createOrReplaceTempView' method.
C) Create a separate session object for each warehouse using a loop and store them in a list. Ensure each session is closed using 'session.close()' in a 'finally' block after use or using a context manager.
D) Create a single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.
E) Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.

5. You are developing a Snowpark application that needs to connect to Snowflake using account identifiers. Your organization's Snowflake account is configured with federated authentication (Okta). Which of the following methods is the most secure and recommended way to establish a Snowpark session in this scenario, avoiding hardcoding credentials in your application and leveraging existing authentication mechanisms?

A) Pass username and password directly in the connection properties along with the account identifier.
B) Use the connection parameter along with username and password directly in the connection properties.
C) Utilize Snowflake's support for OAuth and configure your application to acquire a token from Okta and use it to establish the Snowpark session using the 'authenticator parameter set to 'oauth'.
D) Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
E) Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.

Solutions: