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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

A) Use the available aggregate statistics to create many synthetic datasets, all adhering to the same mean, variance, and total sample size. Then, compute the statistic of interest (mean transaction amount) for each of these synthetic datasets, and use this collection to estimate the standard error. This is a valid approach.
B) Even without individual transaction data, bootstrapping is fundamentally impossible in this scenario, as bootstrapping requires resampling from the original data . All given options are therefore equally inappropriate.
C) Develop a stored procedure that generates random samples from a normal distribution with the same mean and standard deviation as the aggregated transaction data available to you, then calculates the standard error of the mean from these synthetic resamples.
D) Attempt to apply the central limit theorem rather than bootstrapping.
E) Construct a stored procedure that uses the available aggregated statistics (e.g., mean, standard deviation, and sample size) to generate bootstrap samples based on an assumed parametric distribution (e.g., gamma or log-normal) fitted to the data, and then estimate the standard error from these resamples.

2. 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) 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.
C) 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.
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.

3. A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

A) Option C
B) Option B
C) Option E
D) Option A
E) Option D

4. You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

A) 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
B) 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
C) 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
D) 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.
E) 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.

5. A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A) Calculated as 'applicant_income I loan_amount'.
B) Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
C) Calculated as 'applicant_age / applicant_income'.
D) Calculated as 'loan_amount I applicant_age' .
E) Calculated as 'applicant_age applicant_age'.

Solutions: