Try Snowflake : SPS-C01 valid & accurate questions and answers

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?

A)

B)

C)

D)

E)

2. 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) 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.
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) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
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.

3. You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

A) Directly use the method, without any intermediate transformations.
B) Stage 'data_df to an internal stage, then load the data from stage to the table using COPY INTO command.
C) Convert 'data_df to a Pandas DataFrame using and then use to insert the data.
D) Load the DataFrame into chunks of 1000 records into target table by using insert_into() function iteratively
E) Iterate through the rows of 'data_df , constructing and executing a separate INSERT statement for each row using 'session.sql()'.

4. You are developing a Snowpark application that utilizes a DataFrame named 'transactions df containing transactional data. You need to apply a series of complex transformations, including window functions and joins with other DataFrames. To optimize performance and manage resources effectively, you want to control how Snowpark executes these operations within Snowflake. Which of the following actions or configurations would have the MOST significant impact on controlling the execution plan and resource utilization of your Snowpark application?

A) Configure the 'net.snowflake.snowpark.use_native_execution' parameter to 'true' at the session level. This forces Snowpark to translate DataFrame operations into native Snowflake SQL queries.
B) Implement iterative algorithms within your Snowpark application using imperative Python loops instead of declarative DataFrame operations. This provides finer-grained control over the execution flow.
C) Use the 'DataFrame.explain()' method to analyze the generated SQL query plan before executing the transformations. Then, manually optimize the code based on the query plan output.
D) Explicitly cache the 'transactions_df DataFrame using before applying any transformations. This forces Snowpark to materialize the DataFrame in memory.
E) Specify the 'num_partitionS parameter when creating or transforming the 'transactions_df DataFrame. This controls the number of partitions used for parallel processing.

5. You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?

A)

B)

C)

D)

E)

Solutions: