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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?

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

2. A data engineer is tasked with removing duplicates from a table named 'USER ACTIVITY' in Snowflake, which contains user activity logs. The table has columns: 'ACTIVITY TIMESTAMP', 'ACTIVITY TYPE', and 'DEVICE_ID. The data engineer wants to remove duplicate rows, considering only 'USER ID', 'ACTIVITY TYPE, and 'DEVICE_ID' columns. What is the most efficient and correct SQL query to achieve this while retaining only the earliest 'ACTIVITY TIMESTAMP' for each unique combination of the specified columns?

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

3. You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

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

4. You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?

A) The input feature data types in the table do not match the expected data types by the scikit-learn model. Cast the input columns to the correct data types (e.g., FLOAT, INT) before passing them to the UDTF. Use explicit casting functions like 'TO DOUBLE and INTEGER in your SQL query.
B) The issue is related to the immutability of the Snowflake execution environment for UDTFs. To resolve this, cache the loaded model instance within the UDTF's constructor and reuse it for subsequent predictions. Using a global variable is also acceptable.
C) The UDTF is not partitioning data correctly. Ensure the UDTF utilizes the 'PARTITION BY clause in your SQL query based on a relevant dimension (e.g., 'customer_id') to prevent state inconsistencies across partitions. This will isolate the impact of any statefulness within the function
D) There may be an error in model, where the 'predict method is producing different ouputs for the same inputs. Retraining the model will resolve the issue.
E) The scikit-learn model was not properly serialized and deserialized within the UDTF. Ensure the model is saved using 'joblib' or 'pickle' with appropriate settings for cross-platform compatibility and loaded correctly within the UDTF's 'process' method. Verify serialization/deserialization by testing it independently from Snowflake first.

5. You are tasked with fine-tuning a Snowflake Cortex LLM model using your own labeled dataset to improve its performance on a specific sentiment analysis task related to customer reviews. You have already created a Snowflake stage 'my_stage' and uploaded your labeled data in CSV format to this stage. The labeled data contains two columns: 'review_text' and 'sentiment' (values: 'positive', 'negative', 'neutral'). Which of the following SQL commands, or sequences of commands, is MOST appropriate to initiate the fine-tuning process using the 'SNOWFLAKE.ML.FINETUNE LLM' function? Assume you have already set the necessary permissions for your role to access the model and stage.

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

Solutions: