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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

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

Answer: C

Explanation:
Option E is the most efficient and accurate approach. It uses F.try_to_decimar directly in Snowpark to convert the cleaned string (after removing currency symbols using to a NUMERIC(10,2) data type. handles invalid price strings by automatically returning NULL. It avoids the overhead of UDFs and complex conditional logic, streamlining the data preparation process. Option A uses an UDF, which is less efficient than using Snowflake's built-in functions. Option B tries to cast to FloatType instead of Numeric(10,2), not meeting the requirements. Option C is similar to Option B but uses 'to_double' , which doesn't directly address the numeric precision requirement. Option D extracts all the digits and tries to do the if the length is greater than zero.

NEW QUESTION # 21
You are building a fraud detection model using Snowflake data'. The dataset 'TRANSACTIONS' contains billions of records and is partitioned by 'TRANSACTION DATE'. You want to use cross-validation to evaluate your model's performance on different subsets of the data and ensure temporal separation of training and validation sets. Given the following Snowflake table structure:

Which approach would be MOST appropriate for implementing time-based cross-validation within Snowflake to avoid data leakage and ensure robust model evaluation? (Assume using Snowpark Python to develop)

A. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.
B. Create a UDF that assigns each row to a fold based on the 'TRANSACTION DATE column using a modulo operation. This is then passed to the 'cross_validation' function in Snowpark ML.
C. Explicitly define training and validation sets based on date ranges within the Snowpark Python environment, performing iterative training and evaluation within the client environment before deploying a model to Snowflake. No built-in cross-validation used
D. Utilize the 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with the 'input_colS argument containing 'TRANSACTION DATE'. Snowflake will automatically infer the temporal nature of the data and perform time-based cross-validation.
E. Implement a custom splitting function within Snowpark, creating sequential folds based on the 'TRANSACTION DATE column and use that with Snowpark ML's cross_validation. Ensure each fold represents a distinct time window without overlap.

Answer: E

Explanation:
Option E is the most suitable because it explicitly addresses the temporal dependency and prevents data leakage by creating sequential, non-overlapping folds based on 'TRANSACTION DATE. Options A and D rely on potentially incorrect assumptions by Snowflake about time series data and are unlikely to provide the correct cross-validation folds. Option B can introduce leakage because it treats dates as categorical variables and performs random assignment. Option C performs the cross validation entirely outside of Snowflake, which negates the benefits of Snowflake's scalability and data proximity.

NEW QUESTION # 22
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 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).
B. 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.
C. 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).
D. 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.
E. 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.

Answer: A

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 # 23
You are tasked with training a machine learning model within Snowflake using a Python UDTF. The UDTF is intended to process incoming sales data, calculate features, and update the model incrementally. The model is a simple linear regression using scikit-learn. Your initial attempt fails with a 'ModuleNotFoundError: No module named 'sklearn" error within the UDTF. You have already confirmed that scikit-learn is available in your Anaconda channel and specified it during session creation. Which of the following actions would MOST directly address this issue and allow the UDTF to successfully import and use scikit-learn?

A. Recreate the Anaconda environment and ensure that the 'sklearn' package is installed specifically within the environment's 'site-packages' directory. Then, recreate the Snowflake session.
B. When creating the UDTF, use the 'PACKAGES' parameter to explicitly specify the 'skiearn' package. For example: 'CREATE OR REPLACE FUNCTION RETURNS TABLE LANGUAGE PYTHON RUNTIME_VERSION = '3.8' PACKAGES = ('snowflake-snowpark-python','scikit-learn') ...
C. Ensure that the Anaconda channel containing 'sklearn' is explicitly activated at the account level using the 'ALTER ACCOUNT command. Verify the channel is listed in 'SHOW CHANNELS'.
D. Explicitly copy the 'sklearn' directory and its dependencies directly into the same directory as your UDTF definition script on the Snowflake stage, then reference them using relative paths within the UDTF.
E. Include ' import snowflake.snowpark; session = snowflake.snowpark.session.get_active_session()' within the UDTF code to explicitly initialize the Snowpark session before importing sklearn. Ensure that scikit-learn is included in the 'imports' argument of the 'create_dataframe' method.

Answer: B

Explanation:
The 'PACKAGES parameter within the 'CREATE FUNCTION' statement is the MOST direct and reliable way to ensure that specific Python packages are available to your UDTF. Options A, B, and C might address related issues, but directly specifying the package in the function definition is the recommended approach. Option E, although technically feasible, is not a best practice and can lead to dependency management issues. The Snowpark session is automatically created and is not the source of sklearn not being available. The Anaconda environment is a construct that provides the channel information, but the function needs an explict reference to the packages to include within the function body.

NEW QUESTION # 24
You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?

A. Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
B. Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.
C. Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.
D. Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.
E. Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.

Answer: C

Explanation:
The most appropriate approach is to extract the data and perform the confidence interval calculations outside of the stored procedure using a dedicated statistical environment. Options A and D are less scalable and efficient within the stored procedure. Option B provides insufficient information. Option E is not feasible for dynamic calculation based on changing data.

NEW QUESTION # 25
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