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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q192-Q197):

NEW QUESTION # 192
You are working with a Snowflake table 'CUSTOMER DATA containing customer information for a marketing campaign. The table includes columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS, 'CITY, 'STATE, ZIP CODE, 'COUNTRY, 'PURCHASE HISTORY, 'CLICKSTREAM DATA, and 'OBSOLETE COLUMN'. You need to prepare this data for a machine learning model focused on predicting customer churn. Which of the following strategies and Snowpark Python code snippets would be MOST efficient and appropriate for removing irrelevant fields and handling potentially sensitive personal information while adhering to data governance policies? Assume data governance requires removing personally identifiable information (PII) that isn't strictly necessary for the churn model.

A. Drop 'OBSOLETE_COLUMN'. For columns like and 'LAST_NAME' , consider aggregating into a single 'FULL_NAME feature if needed for some downstream task. Apply hashing or tokenization techniques to sensitive PII columns like and 'PHONE NUMBER using Snowpark UDFs, depending on the model's requirements. Drop columns like 'ADDRESS, 'CITY, 'STATE, ZIP_CODE, 'COUNTRY as they likely do not contribute to churn prediction. Example hashing function:
B. Dropping 'FIRST NAME, UST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS', 'CITY, 'STATE', ZIP CODE, 'COUNTRY and 'OBSOLETE_COLUMN' columns directly using 'LAST_NAME', 'EMAIL', 'PHONE_NUMBER', 'ADDRESS', 'CITY', 'STATE', 'ZIP_CODE', 'COUNTRY', without any further consideration.
C. Keeping all columns as is and providing access to Data Scientists without any changes, relying on role based security access controls only.
D. Dropping columns 'OBSOLETE_COLUMN' directly. Then, for PII columns ('FIRST_NAME, 'LAST_NAME, 'EMAIL', 'PHONE_NUMBER, 'ADDRESS', 'CITY', 'STATE' , , 'COUNTRY), create a separate table with anonymized or aggregated data for analysis unrelated to the churn model. Use Keep all PII columns but encrypt them using Snowflake's built-in encryption features to comply with data governance before building the model. Drop 'OBSOLETE COLUMN'.

Answer: C

Explanation:
Option D is the most comprehensive and adheres to best practices. It identifies and removes truly irrelevant columns ('OBSOLETE_COLUMN', and location details), handles PII appropriately using hashing and tokenization (or aggregation), and leverages Snowpark UDFs for custom data transformations. Options A is too simplistic and doesn't consider data governance. Option B is better than A, but more complex than needed if the data is not needed elsewhere. Option C doesn't address the principle of minimizing data exposure. Option E is unacceptable from a data governance and security perspective. The example code demonstrates how to register a UDF for hashing email addresses.

NEW QUESTION # 193
A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

Answer: E

Explanation:
Option E is the MOST robust and efficient. It uses the interquartile range (IQR) method, which is less sensitive to extreme outliers than the z-score method in Option A. It also utilizes 'approx_quantile' and is therefore more optimized for Snowflake large datasets. The median is also a more robust measure of central tendency for imputation than the mean when dealing with outliers. Option C uses a hard-coded threshold for outlier removal and imputes with 0, which is not adaptive or robust. Option D skips data cleaning altogether.Option A uses z-score which may work however, since IoT has continuous streaming data quantile based outlier removal is better. It is more optimised for large dataset and better at handling streaming datasets.

NEW QUESTION # 194
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. Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
B. 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.
C. 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
D. 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.
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.

Answer: B,E

Explanation:
Options B and E represent the most effective strategies. Option B emphasizes in-database processing with a vectorized 'DF and optimized data transfer. Option E highlights the use of 'DFs for preprocessing and leverages GPU acceleration for the computationally intensive inference step, along with efficient model weight caching. Option A introduces unnecessary complexity with external functions, which can add latency. Option C requires additional data storage and management outside of the core classification process. Option D is inefficient because loading the entire table into the 'DF is not scalable and will likely cause performance issues. Vectorizing the 'DF allows for batch processing, which significantly improves throughput. GPU acceleration further enhances the speed of model inference, and caching the model prevents repeated loading, saving computational resources.

NEW QUESTION # 195
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. 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.
B. 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.
C. 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.
D. Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
E. 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.

Answer: A,C,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 # 196
You have a Snowpark DataFrame named 'product_reviews' containing customer reviews for different products. The DataFrame includes columns like 'product_id' , 'review_text' , and 'rating'. You want to perform sentiment analysis on the 'review_text' to identify the overall sentiment towards each product. You decide to use Snowpark for Python to create a user-defined function (UDF) that utilizes a pre-trained sentiment analysis model hosted externally. You need to ensure secure access to this model and efficient execution. Which of the following represents the BEST approach, considering security and performance?

A. Create a Java UDF that utilizes a library to call the sentiment analysis API. Pass the API key as a parameter to the UDF each time it is called.
B. Create an inline Python UDF that directly calls the external sentiment analysis API with hardcoded API keys within the UDF code.
C. Create an external function in Snowflake that calls a serverless function (e.g., AWS Lambda, Azure Function) that performs the sentiment analysis. Use Snowflake's network policies to restrict access to the serverless function and secrets management to handle API keys.
D. Create a Snowpark Pandas UDF that calls the external sentiment analysis API. Use Snowflake secrets management to store the API key and retrieve it within the UDF.
E. Create an external function in Snowflake that calls a serverless function. Configure the API gateway in front of the serverless function to enforce authentication via Mutual TLS (mTLS) using Snowflake-managed certificates.

Answer: E

Explanation:
Option E provides the BEST combination of security and performance. Using an external function that calls a serverless function allows for leveraging scalable compute resources. Configuring the API gateway with Mutual TLS (mTLS) provides a strong layer of authentication, ensuring that only Snowflake can access the serverless function. Snowflake's network policies further restrict access. Storing the API key using Snowflake secrets management within the serverless function provides additional security. Option A is insecure due to hardcoded API keys. Option B is better but can be less performant than external functions. Options C requires managing Java dependencies and might not be as scalable as serverless functions. Option D is good but mTLS gives the best protection available.

NEW QUESTION # 197
......

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Useful Free DSA-C03 Vce Dumps Help You to Get Acquainted with Real DSA-C03 Exam Simulation

Exam DSA-C03 Collection Pdf - Accurate DSA-C03 Test

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q192-Q197):

Pass Guaranteed Quiz 2025 DSA-C03: Newest Free SnowPro Advanced: Data Scientist Certification Exam Vce Dumps

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