Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

A) Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
B) Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
C) Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
D) Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
E) Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.

2. You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

A)

B)

C)

D)

E)

3. You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?

A) Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
B) Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.
C) Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
D) Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.
E) Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.

4. You are training a binary classification model in Snowflake using Snowpark to predict customer churn. The dataset contains a mix of numerical and categorical features, and you've identified that the 'COUNTRY' feature has high cardinality. You observe that your model performs poorly for less frequent countries. To address this, you decide to up-sample the minority classes within the 'COUNTRY' feature before training. Which combination of techniques would be MOST appropriate and computationally efficient for up-sampling in this scenario within Snowflake, considering you are working with a large dataset and want to minimize data shuffling across the network?

A) Use a stored procedure written in Python to iterate through each unique country, identify minority countries, and then use Snowpark to up-sample those countries using 'DataFrame.sample()' with replacement. This offers the most flexibility but introduces significant overhead due to context switching.
B) Utilize Snowflake UDFs (User-Defined Functions) written in Java to perform stratified sampling on the 'COUNTRY' feature, ensuring each minority class is adequately represented in the up-sampled dataset. UDFs allow for complex logic but can be challenging to debug within Snowflake.
C) Leverage Snowpark's 'DataFrame.collect()' to bring the entire dataset to the client machine, then use Python's scikit-learn library for up-sampling. This is suitable only for small datasets as it incurs significant network overhead.
D) Use Snowpark's 'DataFrame.groupBy()" and 'DataFrame.count()' to identify minority countries. Then, for each minority country, use DataFrame.unionByName()' to combine the original data with multiple copies of the minority country's data, created using 'DataFrame.sample()' with replacement. This minimizes data movement within Snowflake.
E) Use the 'SAMPLE clause in Snowflake SQL with 'REPLACE' for each minority country, creating separate temporary tables and then combining them with UNION ALL'. This is efficient for small datasets but scales poorly with high cardinality.

5. You're analyzing the performance of two different AIB testing variants of an advertisement. You've collected the following data over a period of one week: Variant A: 1000 impressions, 50 conversions Variant B: 1100 impressions, 66 conversions Which of the following statements are TRUE regarding confidence intervals and statistical significance in this scenario?

A) Increasing the sample size (number of impressions for each variant) will generally widen the confidence interval, making it more likely to contain zero.
B) Constructing a 95% confidence interval for the difference in conversion rates between Variant B and Variant A will allow you to assess if there is a statistically significant difference at the 5% significance level. If the confidence interval contains zero, there is no statistically significant difference.
C) Calculating separate confidence intervals for conversion rates A and B, and noting overlap, is an invalid method to infer statistical significance. One must construct confidence interval for the difference in means.
D) A narrower confidence interval for the difference in conversion rates implies a higher degree of certainty about the estimated difference.
E) If the 95% confidence interval for the conversion rate of Variant A is entirely above the 95% confidence interval for the conversion rate of Variant B, then Variant A is statistically better than Variant B.

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