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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a large dataset in Snowflake and need to build a machine learning model using scikit-learn in Python. You want to leverage Snowflake's compute resources for feature engineering to speed up the process. Which of the following approaches correctly combines Snowflake's SQL capabilities with scikit-learn for feature engineering and model training, while minimizing data transfer between Snowflake and the Python environment?

A) Write a complex SQL query in Snowmake to perform all feature engineering, then load the resulting features into a Pandas DataFrame and train the scikit-learn model.
B) Create Snowflake User-Defined Functions (UDFs) in Python for complex feature engineering calculations. Call these UDFs within a SQL query to apply the feature engineering to the Snowflake data. Load the resulting features into a Pandas DataFrame and train the scikit-learn model.
C) Use Snowflake external functions to invoke a remote service (e.g., AWS Lambda) for feature engineering. Pass data from Snowflake to the remote service, receive the engineered features back, and load them into a Pandas DataFrame for model training.
D) Implement the feature engineering steps directly in Python using Pandas and scikit-learn, then load the raw data into a Pandas DataFrame and apply the transformations. Finally, train the scikit-learn model.
E) Use the Snowflake Python Connector to execute individual SQL queries for each feature engineering step. Load the resulting features step-by-step into a Pandas DataFrame and train the scikit-learn model.

2. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

3. You are working with a dataset containing customer reviews for various products. The dataset includes a 'REVIEW TEXT column with the raw review text and a 'PRODUCT ID' column. You want to perform sentiment analysis on the reviews and create a new feature called 'SENTIMENT SCORE for each product. You plan to use a UDF to perform the sentiment analysis. Which of the following steps and SQL code snippets are essential for implementing this feature engineering task in Snowflake, ensuring optimal performance and scalability? Select all that apply:

A) Use the 'SNOWFLAKE.ML' package to train a sentiment analysis model directly within Snowflake, eliminating the need for a separate UDF.
B) Ensure the UDF is vectorized to process batches of reviews at once, improving performance. This can be achieved using decorator on top of the python function.
C) Cache the results of the sentiment analysis UDF in a temporary table to avoid recomputing the scores for the same reviews in subsequent queries. Use 'CREATE TEMPORARY TABLE to create a temporary table.
D) Create a Python UDF that takes the 'REVIEW_TEXT as input and returns a sentiment score (e.g., between -1 and 1). Then, use 'CREATE OR REPLACE FUNCTION' statement to register the UDF.
E) Apply the sentiment analysis UDF to the 'REVIEW TEXT column within a 'SELECT statement, grouping by 'PRODUCT ID and calculating the average 'SENTIMENT_SCORE' using

4. A data scientist is analyzing website click-through rates (CTR) for two different ad campaigns. Campaign A ran for two weeks and had 10,000 impressions with 500 clicks. Campaign B also ran for two weeks with 12,000 impressions and 660 clicks. The data scientist wants to determine if there's a statistically significant difference in CTR between the two campaigns. Assume the population standard deviation is unknown and unequal for the two campaigns. Which statistical test is most appropriate to use, and what Snowflake SQL code would be used to approximate the p-value for this test (assume 'clicks_b' , and are already defined Snowflake variables)?

A) An independent samples t-test (Welch's t-test), because we are comparing the means of two independent samples with unequal variances. Snowflake code (approximation using UDF - assuming UDF 'p_value_from_t_stat' exists that calculates p-value from t-statistic and degrees of freedom):

B) A paired t-test, because we are comparing two related samples over time. Snowflake code: 'SELECT t_test_ind(clicks_a/impressions_a, 'VAR EQUAL-TRUE')
C) Az-test, because we know the population standard deviation. Snowflake code: 'SELECT normcdf(clicks_a/impressions_a - clicks_b/impressions_b, O, 1)'
D) A one-sample t-test, because we are comparing the sample mean of campaign A to the sample mean of campaign Snowflake code: 'SELECT t_test_lsamp(clicks_a/impressions_a - clicks_b/impressions_b, 0)'
E) An independent samples t-test, because we are comparing the means of two independent samples. Snowflake code: SELECT

5. A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
B) The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
C) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
D) The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
E) Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.

Solutions: