Pass exam with SPS-C01 Top Exam Collection for sure one-shot

Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A) Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.
B) Use the 'exceptAll' to ensure that there are no schema evolution issues.
C) Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.
D) Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
E) Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.

2. You have a Snowpark DataFrame 'products_df with columns 'product_id', 'category', and 'price'. You want to find the top 3 most expensive products within each category Which of the following Snowpark code snippets will accomplish this, using window functions?

A)

B)

C)

D)

E)

3. 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) 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'.
B) Use the connection parameter along with username and password directly in the connection properties.
C) Create a dedicated Snowflake user with restricted permissions and use its username and password directly in the connection string.
D) Store the username and password in environment variables and retrieve them in your Snowpark application to establish the session.
E) Pass username and password directly in the connection properties along with the account identifier.

4. You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
B) Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
C) Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
D) Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
E) Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.

5. You are developing a Snowpark application that needs to connect to Snowflake using programmatic access. You want to use a secure method of authentication. Which of the following methods, when passed as parameters to the 'snowpark.Session.builder.configS method, would be MOST secure and appropriate for production environments?

A) Using 'oauth_access_token' obtained from an external OAuth server.
B) Passing the 'user', 'password', and 'account' parameters directly as strings.
C) Passing the 'user' and 'password' directly, but retrieving the 'account' from an environment variable.
D) Using 'private_key' stored securely and referencing it using 'private_key_file'.
E) Setting the 'authenticator' parameter to 'snowflake' and rely on default Snowflake authentication mechanism assuming it setup correctly

Solutions: