Snowflake DSA-C03 Exam : SnowPro Advanced: Data Scientist Certification Exam

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. 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)'.
• B. 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.
• C. 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]))'
• D. Read the entire Snowflake table into a single Pandas DataFrame, apply , and then write the entire transformed DataFrame back to Snowflake.
• E. 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.

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You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

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You are training a regression model to predict house prices using a Snowflake dataset. The dataset contains various features, including 'number of_bedrooms', , and You want to use time-based partitioning for your training, validation, and holdout sets. However, you also need to ensure that the dataset is properly shuffled within each time partition to mitigate potential bias introduced by the order of data entry. Which of the following strategies is MOST EFFECTIVE and EFFICIENT for partitioning your data into train, validation, and holdout sets in Snowflake, while also ensuring random shuffling within each partition, and addressing potential data leakage issues?

• A. Create separate views for train, validation, and holdout sets, filtering by 'sale_date' . Shuffle the entire dataset using 'ORDER BY RANDOM()' before creating the views to ensure randomness across all sets. This does not address shuffling within parition.
• B. Create a new column 'split_group' using a CASE statement based on 'sale_date' to assign each row to 'train', 'validation', or 'holdout'. Calculate a random number within each 'split_group' by using OVER (PARTITION BY split_group ORDER BY RANDOM())'. Then create temporary tables for each split using 'CREATE TABLE AS SELECT FROM WHERE split_group = QUALIFY ROW NUMBER() OVER (ORDER BY RANDOM()) (SELECT COUNT( ) FROM transactions WHERE split_group -- ...) (respective split percentage);'
• C. Create a user-defined function (UDF) in Python that takes a 'sale_date' as input and returns either 'train', 'validation', or 'holdout' based on pre-defined date ranges. Apply this UDF to each row, creating a 'split_group' column. Then, create temporary tables for each split using 'CREATE TABLE AS SELECT ... FROM . WHERE split_group = ... ORDER BY RANDOM()'. UDF overhead and global RANDOM sort make it very slow.
• D. Create a new column 'split_group' using a CASE statement based on 'sale_date' to assign each row to 'train', 'validation', or 'holdout'. Then, create temporary tables for each split using 'CREATE TABLE AS SELECT FROM WHERE split_group = ORDER BY RANDOM()'. This can be very slow because of global RANDOM sort and leakage issues with using full dataset for randomness.
• E. Use Snowflake's SAMPLE clause with a 'REPEATABLE seed for each split (train, validation, holdout), filtering by 'sale_date'. Add an 'ORDER BY RANDOM()' clause within each 'SAMPLE query to shuffle the data within each split. This approach does not guarantee non-overlapping sets and can introduce sampling bias.

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You are tasked with performing exploratory data analysis on a table named containing daily sales transactions. The table includes columns like 'transaction_date', 'product_id', 'quantity' , and 'price'. Your goal is to identify potential data quality issues and understand the distribution of sales. Which of the following SQL queries using Snowflake's statistical functions and features would be MOST effective for quickly identifying outliers in the 'quantity' column, potential data skewness, and missing values?

• A. Option E
• B. Option D
• C. Option C
• D. Option A
• E. Option B

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You've deployed a fraud detection model in Snowflake. The model is implemented as a Python UDF that uses a pre-trained scikit-learn model stored as a stage file. Your goal is to enable near real-time fraud detection on incoming transactions. Due to regulatory requirements, you need to maintain a detailed audit trail of all predictions, including the input features, model version, prediction scores, and any errors encountered during the prediction process. Which of the following approaches are valid and efficient for storing these audit logs and predictions in Snowflake?

• A. Create a dedicated table with columns for transaction ID, input features (as a JSON VARIANT), model version, prediction score, error message (if any), and prediction timestamp. Use a Snowflake Sequence to generate unique log IDs.
• B. Use Snowflake's 'SYSTEM$QUERY LOG' table to extract information about the UDF execution and join it with the transaction data to reconstruct the audit trail.
• C. Log the audit information to an external logging service (e.g., Splunk) using an external function called from within the UDF.
• D. Store the audit logs as unstructured text files in an external stage (e.g., AWS S3) and periodically load them into a Snowflake table using COPY INTO command.
• E. Utilize Snowflake's Streams and Tasks to automatically capture changes to the transaction table and trigger the prediction UDF, storing the audit logs in a separate table with similar structure as described in option A.

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