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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame 'df representing sales data with columns 'product_id', 'region', and 'sales_amount'. You want to calculate the total sales amount for each region. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this?

A)

B)

C)

D)

E)

2. You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?

A) Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
B) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
C) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.
D) Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.
E) Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.

3. You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

A) The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.
B) The Anaconda environment is not activated. Activate the environment using 'conda activate
C) The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.
D) The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.
E) The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'

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

5. You're using Snowpark in Python and need to execute a complex SQL query. The query involves several joins and aggregations, and you want to optimize its performance. You are using "session.sql(query)' to execute the query. Which of the following strategies, applied before executing 'session.sql(query)' , would likely lead to the most significant performance improvement for a very large dataset?

A) Create a view of the underlying data source instead of directly querying the table.
B) Use the method on the DataFrame returned by 'session.sql(queryy.
C) Convert the SQL query into a series of Snowpark DataFrame operations (e.g., 'groupBy()', 'agg()').
D) Ensure that the SQL query includes appropriate comments to improve readability.
E) Reduce the size of the data by filtering the DataFrame returned by 'session.sql(queryy using 'where()' before executing any further operations.

Solutions: