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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working on a customer churn prediction project. One of the features you want to normalize is 'customer_age'. However, a Snowflake table constraint ensures that all 'customer_age' values are between 0 and 120 (inclusive). Furthermore, you want to avoid using any stored procedures and prefer a pure SQL approach for data transformation. Considering these constraints, which normalization technique and associated SQL query is the most appropriate in Snowflake for this scenario, guaranteeing that the scaled values remain within a predictable range?

A) Min-Max scaling to the range [0, 1]:

B) Z-score standardization after clipping values outside 1 and 99 percentile:

C) Z-score standardization:

D) Min-Max scaling directly to the range [0, 1] using the known bounds (0 and 120):

E) Box-Cox transformation:

2. A Snowflake table named 'SALES DATA contains a 'TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY' format, and some contain invalid date strings like 'N/A'. You need to standardize all dates to 'YYYY-MM-DD' format and store them in a new column called FORMATTED DATE in a new table 'STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

A) Using a single 'TO_DATE function with format parameter set to 'AUTO' combined with 'TO_VARCHAR to format the date to 'YYYY-MM-DD'.
B) Creating a view on top of 'SALES_DATA' that implements the conversion logic. This avoids creating a new physical table immediately and allows for experimentation with different conversion strategies before materializing the data.
C) Employing Snowpark's error handling mechanism (e.g., 'try...except' blocks) within a loop to iteratively convert each date string, catching and logging errors, and storing valid dates in a new column.
D) Using a Snowpark Python UDF to parse each date string individually, handling different formats with conditional logic, and returning a formatted date string. This provides flexibility in handling diverse date formats.
E) Using a series of DATE" and 'TO_VARCHAR SQL functions in Snowpark to attempt converting the date in different formats and then formatting the result to 'YYYY-MM-DD'. Any conversion failing returns NULL.

3. You are building an automated model retraining pipeline for a sales forecasting model in Snowflake using Snowflake Tasks and Stored Procedures. After retraining, you want to validate the new model against a champion model already deployed. You need to define a validation strategy using the following models: champion model deployed as UDF "FORECAST UDF , and contender model deployed as UDF 'FORECAST UDF NEW'. Given the following objectives: (1) Minimal impact on production latency, (2) Ability to compare predictions on a large volume of real-time data, (3) A statistically sound comparison metric. Which of the following SQL statements best represents how to efficiently compare the forecasts of the two models on a sample dataset and calculate the Root Mean Squared Error (RMSE) to validate the new model?

A)

B)

C)

D)

E)

4. You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?

A) Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.
B) Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
C) Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.
D) Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.
E) Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.

5. 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) 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.
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) 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.
D) 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.
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.

Solutions: