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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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)

2. You are developing a Snowpark application to process images stored in an internal stage. You have defined a Python UDF to detect objects in each image using a pre-trained model. The UDF takes the image file path as input and returns a JSON string containing the detected objects and their bounding boxes. However, you encounter "SerializationError' when running the UDF. Which of the following steps are MOST likely to resolve this issue effectively, assuming the model itself is correctly loaded and functions within the UDF environment?

A) Ensure that the Python environment used for UDF execution has the 'pillow' library installed by specifying it in the 'imports' parameter of the 'create_udf function with the corresponding packages for loading and preprocessing images.
B) Increase the value of the 'MAX MEMORY USAGE parameter for the warehouse to provide more memory for UDF execution. This will prevent running out of resources when processing large images.
C) Serialize the output of the UDF (the JSON string) using a custom serialization function that handles complex data types appropriately, and deserialize it in the Snowpark DataFrame.
D) Reduce the size of the images before passing them to the UDF to reduce memory consumption and serialization overhead. Resize images before ingesting them.
E) Convert the image file path to the image file content using a Snowpark function such as 'snowpark.functions.read' before passing it to the UDF.

3. You are developing a Snowpark application that needs to access data from a Snowflake table called 'EMPLOYEES. You want to create a Snowpark DataFrame representing this table. However, you are facing issues with the connection and believe that the database, schema, or warehouse attributes may not be set up correctly for the session. Which of the following code snippets, used in conjunction, BEST demonstrates how to create a Snowpark session with robust error handling to identify and address potential connection issues before attempting to create the DataFrame?

A)

B)

C)

D)

E)

4. 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) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.
C) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
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.

5. Consider the following Snowpark Python code snippet designed to perform a join operation between two large tables: 'transactions' and 'customers'. The 'transactions' table contains billions of rows and the 'customers' table contains millions of rows. You are experiencing performance bottlenecks during the join operation. The 'transactions' table has a 'customer id' column that references the 'customers' table's primary key 'id'. Which optimization techniques would be MOST effective in improving the join performance within a Snowpark- optimized warehouse?

A) Ensure that the 'customer_id' column in the 'transactions' table and the 'id' column in the 'customers' table have appropriate indexes defined in Snowflake before performing the join.
B) Explicitly specify the join type as a broadcast join using a hint: 'df_transactions.join(df_customers, df_transactions['customer_id'] == df_customers['id'],
C) Repartition the 'transactions DataFrame by the 'customer_id' column and the 'customers' DataFrame by the 'id' column before performing the join using 'df_transactions.repartition('customer_id').join(df_customers.repartition('id'), df_transactions['customer_id'] == df_customers['id'])'.
D) Increase the size of the Snowpark-optimized warehouse to provide more memory and CPU resources for the join operation.
E) Broadcast the smaller DataFrame (customers') to all nodes in the warehouse before performing the join. Use: 'from snowflake.snowpark.functions import broadcast; df_transactions.join(broadcast(df_customers), df_transactions['customer_id']

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