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[General] Vce DSA-C03 Test Simulator & New DSA-C03 Exam Papers

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【General】 Vce DSA-C03 Test Simulator & New DSA-C03 Exam Papers

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q13-Q18):NEW QUESTION # 13
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?
  • A. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
  • B. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
  • C. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
  • D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.  
  • E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
Answer: D
Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.

NEW QUESTION # 14
You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?
  • A. 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
  • B. 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
  • C. 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.
  • D. 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
  • E. 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
Answer: B
Explanation:
Option C is the most comprehensive solution. Box-Cox transformation is effective for skewed data and can handle negative values (if applicable after shifting). Quantile-based transformation maps the transaction frequencies to a uniform distribution, mitigating the impact of outliers. Calculating the difference between the current transaction frequency and the historical average for that day of the week effectively removes seasonality. Logarithmic transformation (A) is a good alternative to Box-Cox but might not be optimal for all skewness types. Winsorization (A) reduces the impact of outliers but doesn't necessarily normalize the data distribution. Standardization (B) is suitable if the data follows a normal distribution, but may not be effective with heavy outliers. Min-max scaling (D) preserves the data distribution, so it is not a remedy for skewed data. Removing outliers (D) can lead to information loss. Replacing outliers with the mean (E) can distort the data distribution.

NEW QUESTION # 15
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 B
  • B. Option D
  • C. Option C
  • D. Option E
  • E. Option A
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 # 16
You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

  • A. Option B
  • B. Option D
  • C. Option C
  • D. Option E
  • E. Option A
Answer: D
Explanation:
Option E is the only fully correct approach. It correctly groups by 'CUSTOMER_ID and finds the maximum transaction date. It calculates the Recency by using 'datediff, , and casting 'LAST_TRANSACTION_DATE' with Without the cast to , it is possible to run into error in 'datediff function. 'datediff function will cause issues when used on a timestamp. The 'recency_sdf dataframe will only have customer_id and recency.

NEW QUESTION # 17
A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

  • A. Option C
  • B. Option B
  • C. Option E
  • D. Option D
  • E. Option A
Answer: A,B
Explanation:
Options B and C correctly calculates the standard deviation. Option B utilizes 'NVL' , which is the equivalent of 'COALESCE or ' IFNULL', to handle NULL Customer Segment values, and 'STDDEV_SAMP' for sample standard deviation, which is generally the correct function to use when dealing with a sample of the entire population. Option C also uses 'COALESCE and utilizes the 'STDDEV POP function, which returns the population standard deviation, assuming the data represents the whole population. Option A uses IFNULL, which works, and STDDEV, which is an alias for either STDDEV SAMP or STDDEV POP. The exact behavior will depend on session variable setting. Option D also uses 'CASE WHEN' construct which works to identify Unknown segments. STDDEV is again aliased. Option E calculates the variance and not Standard deviation.

NEW QUESTION # 18
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