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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline includes a Python UDF that uses a pre- trained scikit-learn model stored as a binary file in a Snowflake stage. The UDF needs to load this model for prediction. You've encountered an issue where the UDF intermittently fails, seemingly related to resource limits when multiple concurrent queries invoke the UDF. Which of the following strategies would best optimize the UDF for concurrency and resource efficiency, minimizing the risk of failure?

A) Increase the memory allocated to the Snowflake warehouse to accommodate multiple UDF invocations.
B) Implement a global, lazy-loaded cache for the scikit-learn model within the UDF's module. The model is loaded only once during the first invocation and shared across subsequent calls. Protect the loading process with a lock to prevent race conditions in concurrent environments.
C) Load the scikit-learn model inside the UDF function on every invocation to ensure the latest version is used.
D) Utilize Snowflake's session-level caching by storing the loaded model in 'session.get('model')' to be reused across multiple UDF calls within the same session. Reload the model if 'session.get('model')' is None.
E) Load the scikit-learn model outside the UDF function in the global scope of the module so that all invocations share the same loaded model instance. Use the 'context.getExecutionContext(Y to track execution, making sure it is thread safe.

2. You are building a predictive model on customer churn using Snowflake data'. You observe that the distribution of 'TIME SINCE LAST PURCHASE' is heavily left-skewed. Which of the following strategies would be MOST appropriate to handle this skewness before feeding the data into a linear regression model to improve its performance? (Select TWO)

A) Apply a logarithmic transformation to the 'TIME SINCE LAST PURCHASE' column.
B) Use a winsorization technique to cap extreme values in the 'TIME SINCE LAST PURCHASE' column at a predefined percentile (e.g., 99th percentile).
C) Remove all records with 'TIME SINCE LAST PURCHASE' values below the mean.
D) Standardize the 'TIME_SINCE_LAST_PURCHASE' column using Z-score normalization.
E) Apply a square root transformation to the 'TIME_SINCE_LAST_PURCHASE' column.

3. A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

4. 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) Attempt to apply the central limit theorem rather than bootstrapping.
B) 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.
C) 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.
D) 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.
E) 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.

5. You are performing exploratory data analysis on a dataset of customer transactions in Snowflake to prepare for a linear regression model that predicts transaction value based on several customer-related features (e.g., age, location, number of previous transactions). You suspect a non-linear relationship between 'customer_age' and 'transaction_value'. Which of the following Snowflake SQL techniques is MOST appropriate for exploring and potentially transforming the 'customer_age' variable to better fit a linear regression model?

A) Apply a logarithmic transformation to 'customer_age' if a scatter plot of 'customer_age' vs 'transaction_value' shows a curve that flattens out as 'customer_age' increases.
B) Calculate the Pearson correlation coefficient between 'customer_age' and 'transaction_value' using the function. If the correlation is low, discard the 'customer_age' variable.
C) Use the window function to bin 'customer_age' into quartiles and treat each quartile as a categorical variable in the linear regression model.
D) Create polynomial features by adding 'customer_ageA2' and 'customer_ageA3' as new columns to the table, without checking for interaction effects.
E) Implement a Box-Cox transformation in Snowpark Python, select a suitable transformation parameter based on the data, and apply the transformation on 'customer_age' feature.

Solutions: