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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

2. You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?

A) Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.
B) Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
C) All of the above.
D) Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.
E) Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.

3. You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

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

4. A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

A) Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.
B) Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
C) Reduce the size of the input DataFrame by sampling the data.
D) Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.
E) Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.

5. You are a data scientist working with a Snowflake table named 'CUSTOMER TRANSACTIONS' that contains sensitive PII data, including customer names and email addresses. You need to create a representative sample of 1% of the data for model development, ensuring that the sample is anonymized and protects customer privacy. The sample must be reproducible for future model iterations.
Which of the following steps are most appropriate using Snowpark for Python and SQL?

A) Use the 'SAMPLE clause in a SQL query to extract 1% of the rows, then apply SHA256 hashing to the 'customer_name' and 'email_addresS columns within Snowpark using a UDF. Seed the sampling for reproducibility.
B) Create a new table using 'CREATE TABLE AS SELECT statement combined with 'SAMPLE clause and SHA256 hashing functions in SQL to create the sample and anonymize data. Manually seed the random number generator in Python before executing the SQL statement via Snowpark.
C) Use the 'QUALIFY OVER (ORDER BY RANDOM()) (SELECT COUNT( ) 0.01 FROM CUSTOMER_TRANSACTIONS)' clause with SHA256 on sensitive columns directly within a CREATE TABLE AS statement to generate an anonymized sample. The function should return only 1 percentage of row.
D) Use Snowpark DataFrame's 'sample' function with a fraction of 0.01 and a fixed random seed. Before sampling, create a view that masks 'customer_name' and 'email_address' columns, and then sample from the view.
E) Employ stratified sampling based on a customer segment column, then anonymize data. Use the TABLESAMPLE BERNOULLI function in SQL with a 1 percent sample rate. Apply SHA256 hashing to the 'customer_name' and 'email_addresS columns using SQL functions.

Solutions: