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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

A)

B)

C)

D)

E)

2. You are building an image classification model within Snowflake to categorize satellite imagery based on land use types (residential, commercial, industrial, agricultural). The images are stored as binary data in a Snowflake table 'SATELLITE IMAGES. You plan to use a pre-trained convolutional neural network (CNN) from a library like TensorFlow via Snowpark Python UDFs. The model requires images to be resized and normalized before prediction. You have a Python UDF named that takes the image data and model as input and returns the predicted class. What steps are crucial to ensure optimal performance and scalability of the image classification process within Snowflake, considering the volume and velocity of incoming satellite imagery?

A) Utilize Snowflake's external functions to call an image processing service hosted on AWS Lambda or Azure Functions for image resizing and normalization, then pass the processed images to the 'classify_image' UDF.
B) Pre-process the images outside of Snowflake using a separate data pipeline and store the resized and normalized images in a new Snowflake table before running the 'classify_image' UDE
C) Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
D) Implement image resizing and normalization directly within the 'classify_image' Python UDF using libraries like OpenCV. Ensure the UDF is vectorized to process images in batches and leverage Snowpark's optimized data transfer capabilities.
E) Use a combination of Snowpark Python UDFs for preprocessing tasks like resizing and normalization, and leverage Snowflake's GPU-accelerated warehouses (if available) to expedite the inference step within the 'classify_image' UDF. Ensure the model weights are efficiently cached.

3. You are working with a Snowflake table named 'CUSTOMER DATA' that contains personally identifiable information (PII), including customer names, email addresses, and phone numbers. Your team needs to perform exploratory data analysis on this data to understand customer demographics and behavior. However, you must ensure that the PII is protected and that only authorized personnel can access the sensitive information. Which of the following strategies should you implement in Snowflake to achieve secure EDA?

A) Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.
B) Apply dynamic data masking to the entire 'CUSTOMER_DATA' table, masking all columns by default, and provide decryption keys only to authorized users.
C) Create a copy of the 'CUSTOMER DATA table without the PII columns and grant 'SELECT' privileges on this copy to the data scientists. Use masking policies on the original table.
D) Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.
E) Create a view on top of that excludes the PII columns (e.g., name, email, phone). Grant 'SELECT privileges on this view to data scientists. Also implement data masking policies on the 'CUSTOMER DATA' table for the PII columns and grant 'SELECT on the table to specific roles requiring access to the masked values.

4. You are working on a fraud detection model and need to prepare transaction data'. You have two tables: 'transactions' (transaction_id, customer_id, transaction_date, amount, merchant_id) and (merchant_id, city, state). You need to perform the following data cleaning and feature engineering steps using Snowpark: 1. Remove duplicate transactions based on 'transaction_id'. 2.
Join the 'transactions' table with the 'merchant_locations table to add city and state information to each transaction. 3. Create a new feature called 'amount_category' based on the transaction amount, categorized as 'Low', 'Medium', or 'High'. 4. The categorization thresholds are defined as follows: 'LoW: amount < 50 'Medium': 50 amount < 200 'High': amount >= 200 Which of the following statements about performing these operations using Snowpark are accurate?

A) The construct in Snowpark can be used to create the 'amount_category' feature directly within the DataFrame transformation without needing a UDF
B) Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories can be completed using the 'when' clause with multiple 'otherwise' clauses.
C) Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories requires use of a User-Defined Function (UDF) as the logic can't be efficiently embedded in a single 'when' clause.
D) You can register SQL UDF to calculate the 'amount_category' using 'CASE WHEN' statement
E) A LEFT JOIN should be used to join the 'transactions' and 'merchant_location' tables to ensure that all transactions are included, even if some merchant IDs are not present in the 'merchant_location' table.

5. You are working with a dataset containing customer reviews for various products. The dataset includes a 'REVIEW TEXT column with the raw review text and a 'PRODUCT ID' column. You want to perform sentiment analysis on the reviews and create a new feature called 'SENTIMENT SCORE for each product. You plan to use a UDF to perform the sentiment analysis. Which of the following steps and SQL code snippets are essential for implementing this feature engineering task in Snowflake, ensuring optimal performance and scalability? Select all that apply:

A) Use the 'SNOWFLAKE.ML' package to train a sentiment analysis model directly within Snowflake, eliminating the need for a separate UDF.
B) Cache the results of the sentiment analysis UDF in a temporary table to avoid recomputing the scores for the same reviews in subsequent queries. Use 'CREATE TEMPORARY TABLE to create a temporary table.
C) Apply the sentiment analysis UDF to the 'REVIEW TEXT column within a 'SELECT statement, grouping by 'PRODUCT ID and calculating the average 'SENTIMENT_SCORE' using
D) Create a Python UDF that takes the 'REVIEW_TEXT as input and returns a sentiment score (e.g., between -1 and 1). Then, use 'CREATE OR REPLACE FUNCTION' statement to register the UDF.
E) Ensure the UDF is vectorized to process batches of reviews at once, improving performance. This can be achieved using decorator on top of the python function.

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