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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

A) Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
B) Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
C) Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.
D) Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.
E) Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.

2. You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

A) When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
B) Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
C) Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
D) Create an associated Stream on the source tables used for Feature Groups
E) Explicitly specify a in the call.

3. You've created a Python UDF in Snowflake that uses the 'numpy' and libraries to perform complex statistical calculations on time-series data'. The UDF is deployed successfully, but when you execute it on a large dataset, you observe significant performance bottlenecks. Analyzing the execution plan reveals that the UDF is being executed serially for each row of the input data, preventing Snowflake from leveraging its parallel processing capabilities. What strategies can you employ to improve the performance and enable parallel execution of the UDF in Snowflake?

A) Rewrite the UDF using Snowflake's Java UDF functionality instead of Python, as Java is inherently faster for numerical computations.
B) Decompose the UDF into smaller, more manageable functions and register each as a separate UDF, hoping Snowflake will parallelize the execution of these smaller UDFs automatically.
C) Modify the UDF to accept a Pandas DataFrame as input instead of individual row values. Ensure your UDF is vectorized to process the entire DataFrame at once.
D) Increase the Snowflake warehouse size to provide more resources for serial execution.
E) Use the 'snowflake.snowpark' library to create a distributed Pandas DataFrame and perform computations directly within the Snowflake engine in a parallel manner.

4. You are building a fraud detection model using Snowflake and discover a severe class imbalance (99% legitimate transactions, 1% fraudulent). You plan to use down-sampling to address this. Which of the following strategies and Snowflake SQL commands would be MOST effective and efficient for down-sampling the majority class (legitimate transactions) in a large Snowflake table named 'TRANSACTIONS before training a model using Snowpark?

A) Create a new table 'BALANCED TRANSACTIONS' by sampling the majority class and combining it with the minority class using UNION ALLS. Use the'SAMPLE clause in Snowflake SQL for efficient sampling:

B) Use Snowpark's function with replacement to create a balanced dataset. This is efficient within the Snowpark environment but might be slower than native SQL sampling for initial data preparation.
C) Manually iterate through the 'TRANSACTIONS' table using a Snowpark 'DataFrame' and randomly select rows from the majority class. This is the most efficient approach for very large tables.
D) Create a new table 'BALANCED_TRANSACTIONS' by sampling the majority class and combining it with the minority class using 'UNION ALL'. Use the 'SAMPLE clause in Snowflake SQL for efficient sampling:

E) Randomly delete rows from the 'TRANSACTIONS table where 'IS FRAUD = FALSE until the class distribution is balanced. This avoids data duplication but can be slow on large tables.

5. 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 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);'
B) 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.
C) 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.
D) 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.
E) 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.

Solutions: