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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q270-Q275):

NEW QUESTION # 270
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. Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.
B. Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.
C. Apply dynamic data masking to the entire 'CUSTOMER_DATA' table, masking all columns by default, and provide decryption keys only to authorized users.
D. 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.
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.

Answer: D,E

Explanation:
Options B and E are both valid strategies. Option B provides a view with non-PII data, while using masking policies on the table. Option E creates a copy of the 'CUSTOMER_DATR table and leverages masking on original table. Option A is insecure. Option C while obfuscating the PII, will lead to data loss and will be costly to move the data. Option D isn't practical, it would overly restrict access.

NEW QUESTION # 271
You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A. Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
B. Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
C. Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
D. Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
E. Force single-threaded execution by setting to avoid overhead associated with parallel processing.

Answer: B,D

Explanation:
Vectorized UDFs and SQL Views are the key to optimizing data pre-processing. Options B and E are correct. B - Utilize Snowpark's vectorized UDFs and DataFrame operations: Snowpark is designed to push computation down to Snowflake's distributed compute engine. Vectorized UDFs allow you to execute Python code in a parallel and efficient manner directly within Snowflake. E - SQL View: Snowpark DataFrame API can query the view from SQL directly. Writing the data preparation logic in SQL leverages the snowflake's engine more effectively than Pandas or Python on a client machine. Options A, C, and D are generally incorrect: Option A is incorrect as it defeats the purpose of using Snowpark. Parallel execution is generally much faster. Option C is incorrect as moving data outside of snowflake is costly. Option D is incorrect. Snowpark is designed to manage a large scale of data.

NEW QUESTION # 272
You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

Answer: E

Explanation:
Option A correctly calculates precision and recall. Precision is calculated as True Positives / (True Positives + False Positives), and Recall is calculated as True Positives / (True Positives + False Negatives). The query in option A directly implements these formulas, where 'PREDICTED CHURN = 1 AND CHURN = 1' represents True Positives. Option B and E calculates accuracy. Option C calculates correlation. Option D calculates Precision and Recall for the negative class (non-churn).

NEW QUESTION # 273
You are working on a customer churn prediction project. One of the features you want to normalize is 'customer_age'. However, a Snowflake table constraint ensures that all 'customer_age' values are between 0 and 120 (inclusive). Furthermore, you want to avoid using any stored procedures and prefer a pure SQL approach for data transformation. Considering these constraints, which normalization technique and associated SQL query is the most appropriate in Snowflake for this scenario, guaranteeing that the scaled values remain within a predictable range?

A. Min-Max scaling to the range [0, 1]:
B. Z-score standardization:
C. Min-Max scaling directly to the range [0, 1] using the known bounds (0 and 120):
D. Z-score standardization after clipping values outside 1 and 99 percentile:
E. Box-Cox transformation:

Answer: C

Explanation:
Option D is the most appropriate. Given the existing constraint on 'customer_age' (0-120), and the requirement to avoid stored procedures, directly scaling to the range [0, 1] using the known minimum and maximum values is efficient and guarantees the output remains within a predictable range. This approach avoids data-dependent calculations (like MIN and MAX over the entire dataset) which are unnecessary given the constraint. Option A won't guarantee values within [0, 1]. Option B is correct but option D is the efficient solution to get the expected outcome and avoid cost and complexity. Option C would not scale to between O and 1 and adds complexity. Option E is not a normalization technique.

NEW QUESTION # 274
You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

A. Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
B. Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.
C. Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.
D. Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
E. Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.

Answer: B,D,E

Explanation:
Options B, C, and D offer the most effective strategies for optimizing performance when training a model incrementally with a Python UDTF in Snowflake. Batching updates (B) reduces the overhead of model updates. Persisting the model to a Snowflake stage (C) decouples training from inference and allows for model reuse. Offloading training to an external function (D) leverages dedicated ML infrastructure. Caching (A) might offer some marginal improvement but is unlikely to address the core performance bottleneck. While Java or Scala (E) can be faster than Python, rewriting the UDTF is a significant undertaking and might not be necessary if other optimization strategies are applied effectively. Also the question is specific about Python. In summary, consider batching and persistence as key in performance optimization.

NEW QUESTION # 275
......

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Bob Evans Bob Evans

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