Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03 valid dump

You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

• A. Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
• B. Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
• C. Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
• D. Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.
• E. Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.

You are developing a data transformation pipeline in Python that reads data from Snowflake, performs complex operations using Pandas DataFrames, and writes the transformed data back to Snowflake. You've implemented a function, 'transform data(df)', which processes a Pandas DataFrame. You want to leverage Snowflake's compute resources for the DataFrame operations as much as possible, even for intermediate transformations before loading the final result. Which of the following strategies could you employ to optimize this process, assuming you have a configured Snowflake connection "conn"?

• A. Create a series of Snowflake UDFs that perform the individual transformations within Snowflake, load the data into Pandas DataFrames, apply UDFs on these DataFrames, and use to upload to Snowflake.
• B. Use Snowpark Python DataFrame API to perform the transformation directly on Snowflake's compute and then load results into the same table. Call 'df_snowpark = session.create_dataframe(df)'.
• C. Use 'snowflake.connector.pandas_tools.write_pandas(conn, df, table_name, auto_create_table=Truey to write the transformed DataFrame to Snowflake and let Snowflake handle the transformations using SQL.
• D. Chunk the Snowflake table into smaller DataFrames using 'fetchmany()' , apply to each chunk, and then append each transformed chunk to a Snowflake table using multiple INSERT statements. Call columns=[col[0] for col in cur.description]))'
• E. Read the entire Snowflake table into a single Pandas DataFrame, apply , and then write the entire transformed DataFrame back to Snowflake.

You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

• A.
  
• B.
  
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• E.
  

A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)

• A. Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
• B. Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
• C. Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
• D. Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.
• E. Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.

You are building a product recommendation system using Snowflake Cortex. You have a table 'PRODUCT DESCRIPTIONS' containing product IDs and textual descriptions. You want to generate vector embeddings for these descriptions to perform similarity searches. However, you need to control the cost and latency of the embedding generation process. Which of the following strategies and considerations are MOST important for optimizing performance and cost when generating vector embeddings in Snowflake Cortex using a UDF?

• A. Use the smallest available Cortex embedding model. Smaller models are always faster and cheaper, regardless of the dataset size.
• B. Cache the results of the embedding LJDF. Implement a caching mechanism (e.g., using a Snowflake table) to store the embeddings for frequently accessed product descriptions, avoiding redundant embedding calculations. use a materialized view.
• C. Optimize the batch size passed to the embedding UDF. Experiment with different batch sizes to find the optimal trade-off between throughput and latency. Too large batches might cause memory issues, while too small batches increase overhead. Consider using a batch size of 64 or 128 as a starting point, adjusting based on your dataset and resource constraints.
• D. Partition the 'PRODUCT DESCRIPTIONS' table by product category and generate embeddings for each partition separately. This helps to distribute the workload and reduce the size of the data processed by each UOF call. This makes more sense and is faster to re-create the table.
• E. Use a larger Snowflake warehouse size. Increasing the warehouse size always linearly reduces embedding generation time and cost.