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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?

A) Ensure that the samples are drawn independently and randomly.
B) Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).
C) Verify that the sample size (n=50) is sufficiently large to approximate normality of the sample mean distribution based on the CLT. This implicitly assumes population size is significantly larger than the sample size.
D) Calculate the standard deviation of the sample means and use it as an estimate for the standard error of the mean.
E) Check if the original population distribution (purchase amounts) is approximately normally distributed.

2. You are tasked with preparing a Snowflake table named 'PRODUCT REVIEWS' for sentiment analysis. This table contains columns like 'REVIEW ID, 'PRODUCT ID', 'REVIEW TEXT', 'RATING', and 'TIMESTAMP'. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the 'PRODUCT REVIEWS' table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.

A) Using 'ALTER TABLE DROP COLUMN' to directly remove 'TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: 'ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
B) Dropping rows with 'NULL' values in REVIEW_TEXT and then dropping the 'PRODUCT_ID' and 'TIMESTAMP' columns using 'ALTER TABLE. SQL: 'CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
C) Creating a VIEW that only selects the 'REVIEW _ TEXT , 'REVIEW_ID', and 'RATING' columns, effectively hiding the irrelevant columns from the model. SQL: 'CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'
D) All of the above.
E) creating a new table 'REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , 'REVIEW_ID' , and 'RATING') using 'CREATE TABLE AS SELECT. SQL: 'CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'

3. You are analyzing customer churn for a telecommunications company. You have a Snowflake table called 'CUSTOMER ACTIVITY with columns 'CUSTOMER ID', 'CALL DURATION_SUM' (total call duration in minutes), 'DATA USAGE GB' (total data usage in GB), 'CONTRACT LENGTH MONTHS', and 'CHURNED' (boolean indicating whether the customer churned). You want to understand the relationship between these features and churn. Specifically, you want to visualize the distribution of 'CALL DURATION SUM' for churned and non-churned customers. Which of the following visualizations, combined with appropriate Snowflake SQL to prepare the data, would BEST illustrate the relationship between 'CALL DURATION SUM' and 'CHURNED'?

A) A scatter plot with on the x-axis and 'CHURNED' (0 or 1) on the y-axis, generated directly from the table using an external visualization tool connected to Snowflake.
B) A pie chart showing the percentage of churned and non-churned customers, with no consideration of 'CALL DURATION SUM'
C) A box plot with 'CHURNED on the x-axis and "CALL DURATION SUM' on the y-axis, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
D) A histogram of 'CALL DURATION SUM" for churned customers and a separate histogram of "CALL DURATION SUM' for non-churned customers, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
E) A line chart plotting the average 'CALL DURATION SUM' over time, ignoring the 'CHURNED' status.

4. You've built a model in Snowflake to predict house prices based on features like location, square footage, and number of bedrooms. After deploying the model, you want to ensure that the incoming data used for prediction is similar to the data the model was trained on. You decide to implement a data distribution comparison strategy. Consider these options and select all that apply:

A) Use Snowflake's built-in statistics functions to compute quantiles (e.g., 25th, 50th, 75th percentiles) for each numerical feature. Compare these quantiles between the training and incoming datasets and set up alerts for significant deviations.
B) Only focus on monitoring the target variable (house price) and assume that if the distribution of house prices remains stable, the input data distribution is also stable.
C) Generate histograms for each numerical feature in both the training and incoming datasets using a Python UDF that leverages libraries like Pandas and Matplotlib. Visually compare the histograms to identify potential distribution shifts.
D) Create a binary classification model in Snowflake that attempts to predict whether a given row of data comes from the training dataset or the incoming dataset. If the model achieves high accuracy, it indicates a significant difference in data distributions.
E) Calculate the mean and standard deviation for each numerical feature in both the training and incoming datasets using Snowflake SQL. Create a Snowflake Alert that triggers if the difference in means or standard deviations exceeds a predefined threshold for any feature.

5. You're building a customer segmentation model and need to aggregate data from various tables. You have the following tables in Snowflake: 'customer demographics' (customer id, age, city, income) 'customer transactionS (transaction_id, customer id, transaction_date, amount) 'product_details' (product_id, category) 'transaction_products' (transaction_id, product_id) Your goal is to create a single Snowpark DataFrame containing customer demographics along with the total amount spent by each customer on products within the 'Electronics' category in the last year. However, ensure that only customers with income greater than 50000 are considered and handle cases where customers have no transaction records, assigning a value of 0 to the 'total_electronics_spending' column for those customers. How can we achieve this using snowpark? Choose the correct options

A) Create a complex SQL query within Snowpark using 'session.sql()' to perform all the joins, filtering, and aggregation in a single step. This will be the most efficient approach.
B) Use a combination of LEFT JOINs and filtering. Start with 'customer_demographics' (filtered for income > 50000) as the base table and LEFT JOIN to subsequent tables. Use the 'coalesce' function to handle customers without transaction data.
C) Use a series of INNER JOINs to connect the tables and filter data, followed by grouping and aggregation. This approach guarantees accurate results with good performance.
D) Create a temporary view to store total electronics expenditure of each customer and left join with customer demographics table.
E) Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.

Solutions: