Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

2. A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

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

3. You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named 'transactions'. You need to create a new feature called 'transaction_size category' based on the 'transaction_amount' column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake's parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?

A)

B)

C)

D)

E)

4. 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)

A) DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.
B) K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.
C) Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.
D) Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.
E) Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.

5. You have trained a machine learning model in Snowflake using Snowpark Python to predict customer churn. You want to deploy this model as a Snowflake User-Defined Function (UDF) for real-time scoring of new customer data arriving in a stream. The model uses several external Python libraries not available by default in the Anaconda channel. Which sequence of steps is the MOST efficient and correct way to deploy the model within Snowflake to ensure all dependencies are met?

A) Create a Snowflake stage, upload the model file and all dependency .py' files. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the 'imports parameter with all the file names. Snowflake will interpret all .py' files as module for UDF execution.
B) Package the model file and all dependencies into a single Python wheel file. Upload this wheel file to a Snowflake stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the wheel file in the 'imports' parameter. Snowflake will automatically install the wheel during UDF execution.
C) Create a Snowflake stage and upload the model file. Create a conda environment file ('environment.yml') specifying the dependencies. Upload the environment.yml file to the stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and the environment.yml file in the 'imports' and 'packages' parameters, respectively. Snowflake will create a conda environment based on the environment.yml file during UDF execution.
D) Create a virtual environment locally with all required dependencies installed. Package the entire virtual environment into a zip file. Upload the zip file to a Snowflake stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the zip file in the 'imports' parameter. Snowflake will automatically extract the zip and use the virtual environment during UDF execution.
E) Create a Snowflake stage, upload the model file and a 'requirements.txt' file listing the dependencies. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the 'imports' parameter with the model file and requirements.txt. Snowflake will automatically install the dependencies from the 'requirements.txt' file during UDF execution.

Solutions: