Snowflake DSA-C03 Actual Free Exam Questions & Community Discussion

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?

You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

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)?

You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

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```

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)

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?

You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?