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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q281-Q286):

NEW QUESTION # 281
You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

A. The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
B. The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
C. The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.
D. The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.
E. The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.

Answer: A,C,D,E

Explanation:
Option A is correct because is the standard method for accessing the active Snowpark session within a stored procedure. Option C is correct as the gsproc' decorator is required to register the function as a Snowpark Stored Procedure, specifying necessary packages. Option D correctly explains how to execute SQL queries using the session object and retrieve results. Option E accurately describes the function's ability to write a Pandas DataFrame to a Snowflake table and create it if it doesn't exist. Option B is incorrect because returns a Snowpark DataFrame, not a Pandas DataFrame. A Snowpark DataFrame is a lazily evaluated representation of the data, while a Pandas DataFrame is an in-memory copy.

NEW QUESTION # 282
You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

A. Option C
B. Option E
C. Option D
D. Option A
E. Option B

Answer: B

Explanation:
The most efficient and accurate way to perform a similarity search with vector embeddings is using ordered in descending order because inner product is the fastest of the vector functions and still gets the vector similarity score. The operator performs an exact match which doesn't consider vector similarity (A). is for array data, not vectors (B). 'QUALIFY' and 'VECTOR COSINE SIMILARITY works but isn't optimal (C), and L2 distance require some value/threshold to compare. 'ORDER BY ... LIMIT is efficient with the inner product, it's very fast (E).

NEW QUESTION # 283
You are tasked with developing a Snowpark Python function to identify and remove near-duplicate text entries from a table named 'PRODUCT DESCRIPTIONS. The table contains a 'PRODUCT ONT) and 'DESCRIPTION' (STRING) column. Near duplicates are defined as descriptions with a Jaccard similarity score greater than 0.9. You need to implement this using Snowpark and UDFs. Which of the following approaches is most efficient, secure, and correct to implement?

A. Define a Python UDF that calculates the Jaccard similarity. Use 'GROUP BY to group descriptions by the 'PRODUCT ID. Apply the UDF on this grouped data to remove duplicates with similarity score greater than threshold.
B. Use the function directly in a SQL query without a UDF. Partition the data by 'PRODUCT_ID' and remove near duplicates where the approximate Jaccard index is above 0.9.
C. Define a Python UDF to calculate Jaccard similarity. Create a temporary table with a ROW NUMBER() column partitioned by a hash of the DESCRIPTION column. Calculate the Jaccard similarity between descriptions within each partition. Filter and remove near duplicates based on a tie-breaker (smallest PRODUCT_ID).
D. Define a Python UDF that calculates the Jaccard similarity. Create a new table, 'PRODUCT DESCRIPTIONS NO DUPES , and insert the distinct descriptions based on the similarity score. Rows in the original table with similar product description must be inserted with lowest product id into new table.
E. Define a Python UDF that calculates the Jaccard similarity between all pairs of descriptions in the table. Use a cross join to compare all rows, then filter based on the Jaccard similarity threshold. Finally, delete the near-duplicate rows based on a chosen tie-breaker (e.g., smallest PRODUCT_ID).

Answer: C

Explanation:
Option D is the most efficient, secure, and correct approach for removing near-duplicate text entries using Snowpark and UDFs. It correctly addresses both the computational complexity and the security implications of the task. - It create a temporary table because we are doing operations of delete and create a table which is best done via temporary table. - It uses bucketing (hashing descriptions) to reduce the number of comparisons. This significantly improves performance compared to comparing all possible pairs of descriptions which is what options A and B do. - Use ROW_NUMBER() to flag duplicate for deletion with threshold. Option A is not optimal due to the complexity of cross join. Option B is incorrect because there is data and functionality that is lost with the insertion of distinct entries based on score. Also, it would be inefficient as it required re-evaluation of score on insertion. Option C is incorrect because Grouping by Product ID will not allow for similarity calculation across different product IDs. Option E is not applicable because Snowflake does not have a built-in 'APPROX JACCARD INDEX' function to apply directly in a SQL query.

NEW QUESTION # 284
You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?

A. Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.
B. Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
C. Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
D. Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.
E. Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.

Answer: A

Explanation:
Option D is the most secure. Key pair authentication is more secure than username/password. Specifying a dedicated virtual warehouse ensures dedicated compute. Option A is highly insecure. Option B doesn't directly create a Snowpark session. Option C, while using OAuth, requires proper setup and key pair provides more control. Option E is highly insecure and grants excessive privileges.

NEW QUESTION # 285
You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)

A. Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
B. Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
C. Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.
D. Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.
E. Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.

Answer: A,D,E

Explanation:
Options A, B, and E are valid and efficient approaches. Option A using 'NTILE' is a direct and efficient way to create quantile bins within Snowflake SQL, and can find the most recent order date for customer with a case statement. Option B calculates the percentiles directly and then uses a CASE statement to assign bins. This is also efficient for explicit boundaries. Option E finds the boundaries of the quantile using 'APPROX_PERCENTILE or 'PERCENTILE_CONT , after that you can use 'WIDTH_BUCKET to categorize into quantile bins based on ranges. Option C is possible but generally less efficient due to the overhead of UDF execution and data transfer between Snowflake and the UDF environment. Option D is valid, but creating a temporary table adds complexity and potentially reduces performance compared to window functions or direct quantile calculation within the query.

NEW QUESTION # 286
......

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Jack West Jack West

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Credible DSA-C03 Exam Dumps bring you the most precise Preparation Questions - ActualVCE

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q281-Q286):

2025 Latest DSA-C03 Exam Questions Vce Free PDF | High Pass-Rate Sample DSA-C03 Test Online: SnowPro Advanced: Data Scientist Certification Exam

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