SnowPro Advanced Real Exam Questions and Answers FREE DSA-C03 Updated on Dec 04, 2025 [Q49-Q72]

SnowPro Advanced DSA-C03 Real Exam Questions and Answers FREE Updated on Dec 04, 2025

DSA-C03 Ultimate Study Guide - ValidDumps

NEW QUESTION # 49
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. 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
• B. Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
• C. 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.
• 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: C,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 # 50
You have trained a classification model in Snowflake using Snowpark ML to predict customer churn. After deploying the model, you observe that the model performs well on the training data but poorly on new, unseen data'. You suspect overfitting. Which of the following strategies can be applied within Snowflake to detect and mitigate overfitting during model validation , considering the model is already deployed and receiving inference requests through a Snowflake UDF?

• A. Create shadow UDFs that score data using alternative models. Compare the performance metrics (such as accuracy, precision, recall) between the production UDF and shadow UDFs using Snowflake's query capabilities. If shadow models consistently outperform the production model on certain data segments, retrain the production model incorporating those data segments with higher weights.
• B. Implement k-fold cross-validation within the Snowpark ML training pipeline using Snowflake's distributed compute. Track the mean and standard deviation of the performance metrics (e.g., accuracy, Fl-score) across folds. A high variance suggests overfitting. Use this information to tune hyperparameters or select a simpler model architecture before deployment.
• C. Calculate the Area Under the Precision-Recall Curve (AUPRC) using Snowflake SQL on both the training and validation datasets. A significant difference indicates overfitting. Then, retrain the model in Snowpark ML with added L1 or L2 regularization, adjusting the regularization strength based on validation set performance, and redeploy the UDF.
• D. Monitor the UDF execution time in Snowflake. A sudden increase in execution time indicates overfitting. Use the 'EXPLAIN' command on the UDF's underlying SQL query to identify performance bottlenecks and rewrite the query for optimization.
• E. Since the model is already deployed, the only option is to collect inference requests and compare the distributions of predicted values in each batch with the predicted values on the training set. A large difference indicates overfitting; model must be retrained outside of the validation process.

Answer: B,C

Explanation:
Options A and C are correct because they describe strategies for detecting and mitigating overfitting during the model validation process using Snowflake's capabilities. AUPRC is a good performance metric to compare the training vs validation set results to catch overfitting, and regularization can be used to avoid overfitting. Option C directly incorporates cross-validation into the model training workflow within Snowflake, allowing for early detection and mitigation of overfitting through hyperparameter tuning and model selection. Option B is incorrect because it focuses on performance optimization, not overfitting. Option D describes an AIB testing or champion-challenger setup which could be a strategy to use to detect data drift over time, but not overfitting. E is only partially correct as it describes one way to detect data drift, but not overfitting.

NEW QUESTION # 51
You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

• A. Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.
• B. Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
• C. Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
• D. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
• E. Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.

Answer: A,E

Explanation:
Options A and E provide the most robust and scalable solutions. A UDF offers flexibility and reusability for data cleaning within Snowpark (Option A). Option E leverages Snowflake's data loading capabilities to clean data during ingestion and adds a UDF for cleaning existing data providing a comprehensive approach. Using a UDF written in Python and used within Snowpark leverages the power of Python's regular expression capabilities and the distributed processing of Snowpark. Handling data transformations during ingestion with Snowflake's built- in COPY INTO with transformation is highly efficient. Option B is less scalable and maintainable for complex formatting. Option C is viable but executing SQL stored procedures from Snowpark Python loses some of the advantages of Snowpark. Option D addresses data masking not data transformation.

NEW QUESTION # 52
You're building a fraud detection model and want to determine if the average transaction amount for fraudulent transactions is significantly higher than the average transaction amount for legitimate transactions. You have two tables in Snowflake:
'FRAUDULENT TRANSACTIONS and 'LEGITIMATE TRANSACTIONS, both with a 'TRANSACTION AMOUNT column. You believe that FRAUDULENT TRANSACTIONS contains fewer than 30 transactions. You don't know the population standard deviations. What are the proper steps to conduct the hypothesis test, and what is the correct hypothesis statement?

• A. Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.
• B. Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.
• C. Perform a chi-squared test. Null Hypothesis: There is no relationship between transaction amount and whether a transaction is fraudulent. Alternative Hypothesis: There is a relationship between transaction amount and whether a transaction is fraudulent.
• D. Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.
• E. Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.

Answer: D

Explanation:
The correct answer is C. Since the sample size for fraudulent transactions is less than 30, and the population standard deviations are unknown, a t-test is more appropriate than a Z-test. The null hypothesis should state the assumption that we are trying to disprove (i.e., fraudulent transactions are not, on average, higher). The alternative hypothesis is the claim we are trying to support (i.e., fraudulent transactions ARE, on average, higher). The chi-squared test is used for categorical data, not continuous data like transaction amount. We are interested in knowing if one set of transaction amounts is greater, so its a one tailed t test.

NEW QUESTION # 53
You are developing a Spark application that needs to read data from a Snowflake table and write the processed data back to a different Snowflake table. Which of the following configurations and code snippets, used in conjunction with the Spark Snowflake Connector, would ensure secure and efficient data transfer, taking into account potential network latency and authentication best practices? Select all that apply.

• A. Employ Snowflake's OAuth authentication. Obtain an OAuth token and pass it as a parameter to the Spark Snowflake Connector. You need to also provide 'sfDatabase' and 'sfSchemas properties. Don't set the number of partitions, leaving it to Spark's default behavior.
• B. Set 'sfURL', 'sfUser', 'sfPassword', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Use to read data and to write data. Rely on Snowflake's default JDBC driver settings for network optimization.
• C. Configure network timeout parameters in the Spark Snowflake Connector options to handle potential network latency, specifically 'networkTimeoutlnMilliSeconds'. Use 'PREACTIONS' and 'POSTACTIONS' to prepare and finalize data loading. Implement robust error handling to retry failed operations.
• D. Use Snowflake's Key Pair Authentication. Store the private key securely, and configure 'sflJRL', 'sflJser', 'private_key', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Ensure the user has appropriate Snowflake privileges. Configure 'numPartitions' parameter based on the scale of data to parallelize read and write operations.
• E.

Answer: C,D

Explanation:
Options B and E are the correct choices. Option B is Key Pair Authentication that is more secure than password authentication and appropriate snowflake previliges ensures proper data access. Setting 'numPartitionS will help optimize I/O during Spark operations. Option E is configuring network timeout parameters handles network latency and 'PREACTIONS' and 'POSTACTIONS provides a better way to manage complex transactions to Snowflake during the Data Load process.

NEW QUESTION # 54
You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

Answer: A

Explanation:
Option D is the most efficient and correct way to calculate RMSE. RMSE is the square root of the average of the squared differences between predicted and actual values. - p.predicted_price), 2)' calculates the squared difference. calculates the average of these squared differences. calculates the square root of the average, resulting in the RMSE. Option A is less efficient because it requires creating a temporary table. Option B and E are incorrect since they uses 'MEAN' which is unavailable in Snowflake and Exp/ln will return geometic mean instead of RMSE. Option C calculates the standard deviation of the differences, not the RMSE.

NEW QUESTION # 55
A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

Answer: A,C,E

Explanation:
Options B, D and E correctly calculate the average daily sales for each month and year. Options B uses 'COALESCE' to replace NULL SALE_AMOUNT values with 0 before calculating the average. Option D utilizes ' NVL', which is a synonym for COALESCE in Snowflake. Option E uses ZEROIFNULL' which is another way to handle NULL values. Option A does not handle NULL values, potentially skewing the average. Option C incorrectly uses "TO_CHAR which results in string format for date, but that is fine, it also tries to use 'IFF which is acceptable to handle Null, but SIFF function may lead to string conversion issues when calculating the average.

NEW QUESTION # 56
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 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.
• B. 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.
• 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 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.
• E. 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.

Answer: E

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 # 57
You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

• A. Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
• B. Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
• C. Increase the size of the training dataset significantly by querying data from external sources.
• D. Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
• E. Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.

Answer: B,D,E

Explanation:
Options A, B, and D are the most effective strategies for addressing multicollinearity and overfitting in this scenario. Ridge Regression (A) adds an L2 regularization term, which penalizes large coefficients and reduces overfitting. Manually removing highly correlated features (B) addresses multicollinearity directly. Performing feature scaling (D) ensures that features with different scales do not disproportionately influence the model. Increasing training data (C) is generally helpful, but doesn't directly solve multicollinearity. Decreasing MAX ITERATIONS (E) might prevent the model from fully converging, but is a less targeted approach than regularization or feature selection.

NEW QUESTION # 58
You are developing a real-time fraud detection system using Snowpark and deploying it as a Streamlit application connected to Snowflake. The system ingests transaction data continuously and applies a pre-trained machine learning model (stored as a binary file in Snowflake's internal stage) to score each transaction for fraud. You need to ensure the model loading process is efficient, and you're aiming to optimize performance by only loading the model once when the application starts, not for every single transaction. Which combination of approaches will BEST achieve this in a reliable and efficient manner, considering the Streamlit application's lifecycle and potential concurrency issues?

• A. Use the 'st.cache_data' decorator in Streamlit to cache the loaded model and Snowpark session. Load the model directly from the stage within the cached function. This approach handles concurrency and ensures the model is only loaded once per session.
• B. Leverage the 'snowflake.snowpark.Session.read_file' to load the model binary directly into a Snowpark DataFrame and then convert to a Pandas DataFrame. Then, use the 'pickle' library for deserialization.
• C. Load the model outside of the Streamlit application's execution context (e.g., in a separate script) and store it in a global variable. Access this global variable within the Streamlit application. This approach requires careful handling of concurrency.
• D. Use Python's built-in 'threading.Lock' to serialize access to the model loading code and the Snowpark session, preventing concurrent access from multiple Streamlit user sessions. Store the loaded model in a module-level variable.
• E. Load the model within a try-except block and set the Snowpark session as a singleton that will guarantee model loads once for the entire application.

Answer: A

Explanation:
Option A is the best approach. 'st.cache_data' is the recommended way to cache data in Streamlit, including large objects like machine learning models. It automatically handles concurrency and ensures the model is only loaded once per Streamlit application instance. Because it's Streamlit's mechanism, it plays well with the Streamlit lifecycle. It is not required to use a Pandas DataFrame like option C suggests. Python global variables (B) are not suitable for web apps due to concurrency issues. While threading locks (D) could work, they add complexity and are generally less desirable than using Streamlit's caching mechanism. The model loading can be cached without a try-except block to set the Snowflake session as a singleton (E).

NEW QUESTION # 59
You are developing a churn prediction model and want to track its performance across different model versions using the Snowflake Model Registry. After registering a new model version, you need to log evaluation metrics (e.g., AUC, F 1-score) and custom tags associated with the training run. Assuming you have a registered model named 'churn_model' with version 'v2', which of the following code snippets demonstrates the correct way to log these metrics and tags using the Snowflake Python Connector and the 'ModelRegistry' API?

• A.
• B.
• C.
• D.
• E.

Answer: C

Explanation:
Option A is correct. It first retrieves the specific model version using , and then calls and 'set_tag' on the returned 'version' object. The other options either attempt to call these methods directly on the "ModelRegistry' object (incorrect as these are version-specific operations) or use incorrect syntax for accessing versions.

NEW QUESTION # 60
You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

• A. Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.
• B. Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.
• C. Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.
• D. Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.
• E. Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.

Answer: B

Explanation:
Storing models in external stages with versioning allows you to easily manage different model versions. Snowflake metadata points to the correct version, and UDFs can load them. Time Travel is useful, but is not ideal for large binary files. Option A is possible, but leads to potentially large and unwieldy Snowflake tables. Option C is not recommended as manual processes can lead to human errors and overwriting active models directly without proper model management creates deployment risks. Deleting older models (option E) prevents rollback.

NEW QUESTION # 61
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 C
• B. Option B
• C. Option D
• D. Option A
• E. Option E

Answer: E

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 # 62
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. Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
• C. 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.
• D. Force single-threaded execution by setting to avoid overhead associated with parallel processing.
• E. 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.

Answer: B,C

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 # 63
You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

• A. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
• B. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
• C. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
• D. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
• E. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.

Answer: B,D,E

Explanation:
The correct answers are B, D, and E. B: One-hot encoding allows you to see the individual effect of each neighborhood. D: ICE plots reveal how the relationship between 'location' and predicted price varies for different individual instances, highlighting potential heterogeneity. E: A two-way PDP with 'location' and 'square_footage' helps understand if the effect of location is different for houses of different sizes. Removing 'location' (option A) might decrease performance if it's a relevant feature. Replacing it with average price (option C) introduces potential bias and data leakage if the historical data is used for both training and validation.

NEW QUESTION # 64
A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

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

Answer: E

Explanation:
Both options B and D achieve the desired label encoding and handle NULL values correctly. Option B creates a new table based on the transformed dataset, which is a common approach for preserving the original data. Option D directly updates the existing table which could be appropriate depending on the team and security structure.

NEW QUESTION # 65
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?

• A.
• B.
• C.
• D.
• E.

Answer: A

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 # 66
A data science team at a retail company is using Snowflake to store customer transaction data'. They want to segment customers based on their purchasing behavior using K-means clustering. Which of the following approaches is MOST efficient for performing K-means clustering on a very large customer dataset in Snowflake, minimizing data movement and leveraging Snowflake's compute capabilities, and adhering to best practices for data security and governance?

• A. Using a Snowflake User-Defined Function (UDF) written in Python that leverages the scikit-learn library within the UDF to perform K-means clustering directly on the data within Snowflake. Ensure the UDF is called with appropriate resource allocation (WAREHOUSE SIZE) and security context.
• B. Implementing K-means clustering using SQL queries with iterative JOINs and aggregations to calculate centroids and assign data points to clusters. This approach is computationally expensive and not recommended for large datasets. Moreover, security considerations are minimal.
• C. Employing only Snowflake's SQL capabilities to perform approximate nearest neighbor searches without implementing the full K-means algorithm. This compromises the accuracy and effectiveness of the clustering results.
• D. Using Snowflake's Snowpark DataFrame API with a Python UDF to preprocess the data and execute the K-means algorithm within the Snowflake environment. This approach allows for scalable processing within Snowflake's compute resources with data kept securely within the governance boundaries.
• E. Exporting the entire customer transaction dataset from Snowflake to an external Python environment, performing K-means clustering using scikit-learn, and then importing the cluster assignments back into Snowflake as a new table. This approach involves significant data egress and potential security risks.

Answer: D

Explanation:
Snowpark and Python UDFs provide a way to execute code within the Snowflake environment, leveraging its compute resources and keeping data within Snowflake's security and governance boundaries. This avoids data egress and is more efficient than exporting data or attempting to implement K-means directly in SQL. While B is potentially viable, D leveraging DataFrames provides further optimization. The other options are either inefficient or insecure.

NEW QUESTION # 67
You are working with a Snowflake table named 'sensor readingS containing IoT sensor data'. The table has columns 'sensor id' , 'timestamp' , and 'reading value'. You observe that the 'reading value' column contains a significant number of missing values (represented as NULL). To prepare this data for a time series analysis, you need to impute these missing values. You have decided to use the 'LOCF' (Last Observation Carried Forward) method, filling the NULL values with the most recent non-NULL value for each sensor. In addition to LOCF, you also want to handle the scenario where a sensor has NULL values at the beginning of its data stream (i.e., no previous observation to carry forward). For these initial NULLs, you want to use a fixed default value of 0. Which of the following approaches, using either Snowpark for Python or a combination of Snowpark and SQL, correctly implements this LOCF imputation with a default value?

• A.
• B. All of the above
• C.
• D.
• E.

Answer: C,D,E

Explanation:
Options A, B, and C all correctly implement LOCF imputation with a default value of 0 for initial NULLs. Option A first uses ignorenulls=TrueV within a window to perform LOCF and then uses 'fillnas to replace any remaining NULLs (the initial NULLs) with 0. Option B is the most concise, using 'coalesce' to combine the LOCF result with the default value of 0. 'coalesce' returns the first non-NULL value in a list of expressions. Option C implements the same logic using SQL within Snowpark. The function performs LOCF, and 'COALESCE provides the default value. Option D uses 'F.lag' , which retrieves the previous value, not the last value carried forward. Therefore, it will not perform LOCF correctly.

NEW QUESTION # 68
You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?

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

Answer: A

Explanation:
Option D is the most complete. It correctly uses 'Trials' to store results, ensures reproducibility with 'rstate' (important for controlled experiments), and demonstrates the correct way to save the trials to a Snowflake table using session.createDataFrame(trials.trials).write.save_as_table('HYPEROPT TRIALS')'. Option C also attempts to save results but saves 'trials.trials', not 'trials.results'. 'trials.trials' contains more detailed information for the hyperopt run. Reproducibility is also not ensured, which makes Option D slightly preferable. SparkTrials is only used for Spark not Snowflake, thus eliminating Option E. Option A does not store the output, and Option B saves 'trials.results' but lacks reproducibility and only processes 'trials.results'.

NEW QUESTION # 69
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 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).
• B. 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).
• C. 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.
• D. 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.
• 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: B

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 # 70
A Data Scientist is designing a machine learning model to predict customer churn for a telecommunications company. They have access to various data sources, including call logs, billing information, customer demographics, and support tickets, all residing in separate Snowflake tables. The data scientist aims to minimize bias and ensure data quality during the data collection phase. Which of the following strategies would be MOST effective for collecting and preparing the data for model training?

• A. Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.
• B. Directly use all available columns from each table without any preprocessing to avoid introducing bias.
• C. Create a single, wide table by performing a series of INNER JOINs on all tables using customer ID as the primary key. Handle missing values by imputing with the mean for numerical columns and 'Unknown' for categorical columns.
• D. Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
• E. Perform exploratory data analysis (EDA) on each table to identify relevant features and potential biases. Use feature selection techniques to reduce dimensionality. Implement robust data validation checks to ensure data quality and consistency before joining the tables. Handle missing values strategically based on the specific column and its potential impact on the model.

Answer: E

Explanation:
Option C is the MOST effective because it emphasizes a thorough and rigorous approach to data collection and preparation. It highlights the importance of EDA for identifying relevant features and biases, feature selection for dimensionality reduction, data validation for ensuring data quality, and strategic handling of missing values. This approach helps to minimize bias, improve model performance, and ensure the reliability of the churn prediction model. The other options are flawed because they either ignore potential biases and data quality issues (A), use a simplistic approach to handling missing values (B), compromise data representativeness (D), or introduce potentially irrelevant data (E).

NEW QUESTION # 71
You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

• A. Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.
• B. Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
• C. Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
• D. Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
• E. Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.

Answer: B,D,E

Explanation:
Options A, B, and C are the best choices for low-latency, high-throughput fraud detection. Option A: Using Snowflake Tasks with the 'WHEN SYSTEM$STREAM HAS DATA()' clause ensures that tasks only run when there is new data in the stream, enabling incremental processing and reducing unnecessary computations. Option B: Dynamic Tables automatically update as the underlying data changes, providing continuously transformed features. Increasing the 'WAREHOUSE SIZE ensures enough compute resources are available. Option C: Snowpark ML UDFs allow you to score incoming transactions in near real-time, leveraging Snowflake's vectorized engine for fast performance. Option D introduces a delay of one hour for model retraining, which is not ideal for a real-time system. Furthermore, regularly scheduling retrain tasks and rerunning them when data is available from stream is not most efficient processing paradigm. While option E is relevant, the question focuses on the transformation, model scoring and data processing parts of a real time data sciecne pipeline, for which A, B, and C are more directly connected.

NEW QUESTION # 72
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