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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q283-Q288):

NEW QUESTION # 283
A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

Answer: C

Explanation:
Option D is the most efficient and accurate because it uses a single SQL query to calculate the MSE directly. It avoids using cursors or procedural logic, which are less performant in Snowflake. It uses SUM to calculate the sum of squared errors and COUNT( ) to get the total number of records, then divides to obtain the average (MSE). Option B calculates the average of power, that is wrong mathematical operation, Option A is correct from mathematical point but slow because of cursor and not following Snowflake best practices, option C is using JavaScript which is also valid, but Snowflake recommends to use SQL when possible for performance, and option E is using external python for model calculation, that not best for this scenarios.

NEW QUESTION # 284
A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A. Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
B. Redefine the function to accept string arguments and cast them to the correct data types within the function.
C. Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
D. Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),
E. Add '@F.sproc' decorator before the function definition.

Answer: D

Explanation:
The error message 'UDFArgumentException: Invalid argument types for function 'calculate_engagement_score_udf. Expected arguments: ONT, FLOAT, INT], actual arguments: [COLUMN_NAME, COLUMN_NAME, COLUMN_NAME]' indicates that the UDF is receiving column objects instead of the actual data values. This is because when calling the UDF on a Snowpark DataFrame, you need to explicitly reference the columns using The correct way to apply the UDF to the DataFrame is to use the 'select' function with ' F.col()' to pass the column objects as arguments to the UDF.

NEW QUESTION # 285
A Snowflake table named 'SALES DATA contains a 'TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY' format, and some contain invalid date strings like 'N/A'. You need to standardize all dates to 'YYYY-MM-DD' format and store them in a new column called FORMATTED DATE in a new table 'STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

A. Using a Snowpark Python UDF to parse each date string individually, handling different formats with conditional logic, and returning a formatted date string. This provides flexibility in handling diverse date formats.
B. Using a single 'TO_DATE function with format parameter set to 'AUTO' combined with 'TO_VARCHAR to format the date to 'YYYY-MM-DD'.
C. Creating a view on top of 'SALES_DATA' that implements the conversion logic. This avoids creating a new physical table immediately and allows for experimentation with different conversion strategies before materializing the data.
D. Employing Snowpark's error handling mechanism (e.g., 'try...except' blocks) within a loop to iteratively convert each date string, catching and logging errors, and storing valid dates in a new column.
E. Using a series of DATE" and 'TO_VARCHAR SQL functions in Snowpark to attempt converting the date in different formats and then formatting the result to 'YYYY-MM-DD'. Any conversion failing returns NULL.

Answer: C,E

Explanation:
Options B and D are the most effective. Option B uses with different formats to handle inconsistencies. If a format fails, it returns NULL, providing a clean way to handle invalid dates. Combining this with VARCHAR formats the valid dates to 'YYYY-MM-DD'. Option D suggests creating a view. Views are useful for testing transformation logic without immediately impacting the base table, allowing experimentation before committing to a data transformation pipeline. Materializing the data into a table would be a subsequent step, after verifying the transformation's correctness. Option A, while flexible, is less performant because UDFs (User-Defined Functions) generally add overhead compared to built-in SQL functions. Option C is inefficient and not a recommended practice in Snowpark for vectorized operations. Option E will not work in most of the cases, as the AUTO parameter cannot reliably differentiate all provided formats. Furthermore, it does not account for data quality issues where there is no date format.

NEW QUESTION # 286
You are tasked with automating the retraining of a fraud detection model in Snowflake. The model is deployed as a Snowflake User-Defined Function (UDF). The training data resides in a Snowflake table named 'TRANSACTIONS. You want to trigger retraining if the model's performance, as measured by AUC, drops below 0.80. The model's AUC is tracked in a Snowflake table named 'MODEL PERFORMANCE. Which of the following strategies provides the MOST efficient and robust solution for automating this retraining process within Snowflake, minimizing latency and external dependencies?

A. Use a Snowflake Task that executes a stored procedure. The stored procedure queries 'MODEL PERFORMANCE, and if the AUC is below 0.80, it triggers a Data Engineering pipeline (e.g., using Airflow or Databricks) to retrain the model and update the UDF.
B. Implement a Snowflake Task that executes a stored procedure. The stored procedure queries 'MODEL_PERFORMANCE. If the AUC is below 0.80, it executes a Snowflake ML pipeline using 'snowflake.ml.modeling' to retrain the model directly within Snowflake and updates the UDF in place using 'CREATE OR REPLACE FUNCTION'.
C. Create an external function that is invoked periodically by a Snowflake Task. The external function queries 'MODEL PERFORMANCE and uses a cloud provider's machine learning service (e.g., AWS SageMaker) to retrain the model and update the UDF using Snowflake's external functions capabilities for model deployment.
D. Manually monitor on a dashboard and trigger retraining via a Snowflake Worksheet when needed.
E. Schedule a job on an external system (e.g., a cron job on a Linux server) to periodically query 'MODEL PERFORMANCE and trigger a model retraining process if the AUC is below 0.80. This process would retrain the model externally and update the UDF in Snowflake.

Answer: B

Explanation:
Option C is the most efficient and robust solution. It leverages Snowflake's built-in Task capabilities and Snowflake ML to retrain the model directly within Snowflake. This minimizes latency, eliminates external dependencies, and keeps the entire process within the Snowflake environment. Options A, B, and D introduce external dependencies, increasing complexity and potential points of failure. Option E is not automated and therefore not a viable solution for automated retraining. Snowflake ML streamlines the retraining process and ensures consistency with the initial model development environment. Using 'CREATE OR REPLACE FUNCTION' ensures the UDF is updated atomically.

NEW QUESTION # 287
You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

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

Answer: D

Explanation:
Option E is the correct answer because it correctly calculates the RMSE using the Snowflake ML PREDICT function in conjunction with the POWER and AVG functions within a SQL query. It constructs an object for input to PREDICT, excluding the actual price to prevent data leakage. Options A, B, and C have syntax errors or incorrect function usage for calculating RMSE in Snowflake and assume a PREDICT function that is generated by CREATE SNOWFLAKE.ML.FORECAST, they don't uses SNOWFLAKE.ML.PREDICT directly. Option D assumes a function named ROOT MEAN SQUARED ERROR which is not a native Snowflake function.

NEW QUESTION # 288
......

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Top Reliable DSA-C03 Cram Materials & Perfect Accurate DSA-C03 Test & Fantastic Reliable DSA-C03 Test Practice

Accurate Snowflake DSA-C03 Test & Reliable DSA-C03 Test Practice

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q283-Q288):

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