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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is building a churn prediction model using Snowflake data'. They want to load a large dataset (50 million rows) from a Snowflake table 'customer_data' into a Pandas DataFrame for feature engineering. They are using the Snowflake Python connector. Given the code snippet below and considering performance and memory usage, which approach would be the most efficient for loading the data into the Pandas DataFrame? Assume you have a properly configured connection and cursor 'cur'. Furthermore, assume that the 'customer id' column is the primary key and uniquely identifies each customer. You are also aware that network bandwidth limitations exist within your environment. ```python import snowflake.connector import pandas as pd # Assume conn and cur are already initialized # conn = snowflake.connector.connect(...) # cur = conn.cursor() query = "SELECT FROM customer data```

A) ```python cur.execute(query) df = pd.read_sql(query, conn)
B) ```python import snowflake.connector import pandas as pd import pyarrow import pyarrow.parquet # Enable Arrow result format conn.cursor().execute("ALTER SESSION SET PYTHON USE ARROW RESULT FORMAT-TRUE") cur.execute(query) df =
C) ```python with conn.cursor(snowflake.connector.DictCursor) as cur: cur.execute(query) df = pd.DataFrame(cur.fetchall())
D) ```python cur.execute(query) results = cur.fetchmany(size=1000000) df_list = 0 while results: df_list.append(pd.DataFrame(results, for col in cur.description])) results = cur.fetchmany(size=1000000) df = pd.concat(df_list, ignore_index=True)
E) ```python cur.execute(query) df = pd.DataFrame(cur.fetchall(), columns=[col[0] for col in cur.description])

2. You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?

A)

B)

C)

D)

E)

3. You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

A) Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.

B) Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
C) The best way to calculate AUC is to randomly guess the probabilities and see how it performs.
D) The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
E) Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.

4. You've built a model in Snowflake to predict the likelihood of a customer clicking on an advertisement. The model outputs a probability score between 0 and 1. You want to determine the optimal threshold to use for converting these probabilities into binary predictions (click/no-click). Your business stakeholders have provided the following information: Cost of showing an ad: $0.10; Revenue generated from a click: $1.00; You have access to a table 'AD_PREDICTIONS' with columns 'CUSTOMER_ID', 'PREDICTED_PROBABILITY' , and 'ACTUAL CLICK' (1 for click, 0 for no click). Which of the following approaches would be the MOST appropriate for selecting the optimal probability threshold to maximize profit, and why?

A) Use the precision-recall curve to find the threshold that maximizes the F1 -score, balancing precision and recall.
B) Iterate through a range of probability thresholds (e.g., 0.01 to 0.99), and for each threshold, calculate the profit using SQL in Snowflake: 'SELECT SUM(CASE WHEN PREDICTED PROBABILITY threshold THEN CASE WHEN ACTUAL CLICK = 1 THEN 0.9 ELSE -0.1 END ELSE O END) AS Profit FROM AD_PREDICTIONS;' Choose the threshold that maximizes the profit.
C) Calculate the point on the ROC curve closest to the top-left corner (perfect classification) and use the corresponding threshold. This optimizes for both sensitivity and specificity.
D) Select a threshold of 0.5, as this is a common default threshold for binary classification problems.
E) Select a very high probability threshold (e.g., 0.9) to ensure that only the most likely clicks are targeted, minimizing wasted ad spend.

5. You're working with a large dataset containing customer purchase history. You want to identify customers whose purchase frequency deviates significantly from the average purchase frequency of all customers. The dataset is in a table named 'purchase history' with columns 'customer id' and 'purchase date'. What combination of Snowflake functionalities will allow you to achieve this task efficiently?
Choose all that apply.

A) Calculate the average purchase frequency across all customers using and group by 'customer_id'.
B) Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
C) Calculate the Z-score of each customer's purchase frequency using 'AVG(Y, 'STDDEV()' , and window functions, and then filter based on a Z-score threshold.
D) Create a UDF that computes the purchase frequency for a single user and apply it to all customers.
E) Use the window function to divide customers into quantiles based on their total purchase count.

Solutions: