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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?

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

2. You are analyzing website clickstream data stored in Snowflake to identify user behavior patterns. The data includes user ID, timestamp, URL visited, and session ID. Which of the following unsupervised learning techniques, combined with appropriate data transformations in Snowflake SQL, would be most effective in discovering common navigation paths followed by users? (Choose two)

A) Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.
B) Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.
C) DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.
D) K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.
E) Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.

3. You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A) Revert to a previous version of the model known to have performed well, while investigating the issue in the background.
B) Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
C) Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
D) Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
E) Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.

4. 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 temporary view to store total electronics expenditure of each customer and left join with customer demographics table.
B) Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.
C) 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.
D) 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.
E) 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.

5. You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?

A) Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
B) Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.
C) Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
D) Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.
E) Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.

Solutions: