Snowflake DSA-C03

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?

A) Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.
B) Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.
C) Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
D) Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.
E) Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.

2. A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A) Add '@F.sproc' decorator before the function definition.
B) Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
C) Redefine the function to accept string arguments and cast them to the correct data types within the function.
D) Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
E) Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),

3. You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A) Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.
B) Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
C) Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
D) Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
E) Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.

4. You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?

A)

B) None of the above
C)

D)

E)

5. You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A) 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.
B) 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
C) 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').
D) 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
E) 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.

Solutions: