[May 30, 2026] Powerful DSA-C03 PDF Dumps for DSA-C03 Questions [Q51-Q70]

[May 30, 2026] Powerful DSA-C03 PDF Dumps for DSA-C03 Questions

Authentic DSA-C03 Dumps - Free PDF Questions to Pass

NEW QUESTION # 51
A data scientist is tasked with predicting house prices using Snowflake. They have a dataset stored in a Snowflake table called 'HOUSE PRICES' with columns such as 'SQUARE FOOTAGE, 'NUM BEDROOMS, 'LOCATION_ID, and 'PRICE. They choose a Random Forest Regressor model. Which of the following steps is MOST important to prevent overfitting and ensure good generalization performance on unseen data, and how can this be effectively implemented within a Snowflake-centric workflow?

• A. Increase the number of estimators (trees) in the Random Forest to the maximum possible value to capture all potential patterns, without cross validation.
• B. Randomly select a small subset of the features (e.g., only use 'SQUARE FOOTAGE and 'NUM BEDROOMS) to simplify the model and prevent overfitting.
• C. Tune the hyperparameters of the Random Forest model (e.g., 'max_deptm, 'n_estimators') using cross-validation. You can achieve this by splitting the 'HOUSE PRICES table into training and validation sets using Snowflake's 'QUALIFY clause or temporary tables, then train and evaluate the model within a loop or stored procedure.
• D. Train the Random Forest model on the entire 'HOUSE PRICES table without splitting into training and validation sets, as this will provide the model with the most data.
• E. Eliminate outliers without understanding the data properly to reduce noise.

Answer: C

Explanation:
Hyperparameter tuning with cross-validation is crucial to prevent overfitting. By splitting the data into training and validation sets, we can evaluate the model's performance on unseen data and adjust the hyperparameters accordingly. Snowflake's 'QUALIFY' clause and temporary tables can be used to efficiently manage these splits. Using a maximum number of estimators without validation is prone to overfitting. Training on the entire dataset without validation provides no indication of generalization performance. Randomly selecting a subset of features may remove important predictors and eliminating outliers without proper investigation can skew your data and reduce the efficacy of the model.

NEW QUESTION # 52
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. Add '@F.sproc' decorator before the function definition.
• 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. Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.

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 # 53
You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

• A. Implement a clustering algorithm (e.g., DBSCAN) within Snowflake using Snowpark Python to group similar sensor readings, identifying outliers as points that do not belong to any cluster or belong to very small clusters.
• B. Calculate basic statistical summaries (mean, standard deviation, min, max) for each sensor and each variable C TEMPERATURE, 'PRESSURE, and 'VIBRATION') and use that information to filter down to the most important sensor, prior to using the other techniques.
• C. Calculate Z-scores for 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' for each 'SENSOR_ID within a rolling window of the last 24 hours using Snowflake's window functions. Define outliers as readings with Z-scores exceeding a threshold (e.g., 3).
• D. Directly connect the 'SENSOR_READINGS' table to a visualization tool and create a 3D scatter plot with 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' on the axes, without any pre-processing or outlier detection in Snowflake.
• E. Create a Snowflake stored procedure to automatically flag outlier readings in a new column 'IS OUTLIER based on a predefined rule set (e.g., IQR method or Z-score threshold), and then use this column to filter data for visualization in a dashboard.

Answer: A,B,C,E

Explanation:
Options A, C, D, and E are essential. A (Z-score calculation with rolling window) provides a dynamic measure of how unusual a reading is relative to recent history for each sensor. C (DBSCAN clustering) helps identify outliers based on density; points far from any cluster are likely outliers. D (Stored procedure with outlier flagging) automates the outlier detection process and makes it easy to filter and visualize outliers in a dashboard, with a business ready explanation. Option E allows you to focus on the right data, allowing you to have a more useful visualisation. Option B (direct 3D scatter plot without pre-processing) is not effective because it will be difficult to identify outliers visually in a high- density scatter plot without any outlier detection or data reduction. The direct scatter plot becomes overwhelming very quickly with sensor data.

NEW QUESTION # 54
A financial institution aims to detect fraudulent transactions using a Supervised Learning model deployed in Snowflake. They have a dataset with transaction details, including amount, timestamp, merchant category, and customer ID. The target variable is 'is_fraudulent' (0 or 1). They are considering different Supervised Learning algorithms. Which of the following algorithms would be MOST suitable for this fraud detection task, considering the need for interpretability, scalability, and the potential for imbalanced classes, and what specific strategies can be employed within Snowflake to handle the class imbalance?

• A. Support Vector Machine (SVM) with a radial basis function (RBF) kernel, as it can capture complex non-linear relationships without concern for interpretability.
• B. K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.
• C. Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.
• D. Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.
• E. Decision Tree or Random Forest, combined with techniques like oversampling the minority class (fraudulent transactions) within Snowflake using SQL or UDFs to balance the dataset before training. These models provide reasonable interpretability and can handle non-linear relationships effectively.

Answer: E

Explanation:
Decision Trees and Random Forests are well-suited for fraud detection due to their ability to handle non-linear relationships and provide interpretability. The class imbalance problem (where fraudulent transactions are much rarer than legitimate ones) is a common challenge in fraud detection. Oversampling the minority class or using techniques like SMOTE within Snowflake before training can significantly improve the model's performance. KNN is not well-suited for high-dimensional data or imbalanced datasets. SVM can be computationally expensive and lacks interpretability. Linear Regression is inappropriate for a classification problem. Naive Bayes makes strong independence assumptions that may not hold in fraud detection scenarios.

NEW QUESTION # 55
A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a 'Transaction_Amount' column. They want to binarize this feature, creating a new 'ls_High_Value' column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?

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

Answer: C

Explanation:
The ' IIF function in Snowflake provides a concise and efficient way to perform conditional logic. It's specifically designed for this type of binary assignment. Options A would not handle NULL values correctly, potentially resulting in NULL 'ls_High_Value' entries. Options B and C are correct, but using a Numeric column (Option D) might be preferred in some ML models. Options E is more complex and less readable for a simple binarization task. Therefore, option D using IIF for a numeric binarized column, making it preferable in some scenarios for ML training.

NEW QUESTION # 56
You are responsible for deploying a fraud detection model in Snowflake. The model needs to be validated rigorously before being put into production. Which of the following actions represent the MOST comprehensive approach to model validation within the Snowflake environment, focusing on both statistical performance and operational readiness, and using Snowflake features for validation?

• A. Calculating only the AUC (Area Under the Curve) metric on the entire dataset without performing any data splitting or cross-validation. Deploying the model if the AUC is above 0.7.
• B. Implementing K-fold cross-validation using Snowflake stored procedures and temporary tables to store and aggregate the results from each fold. Evaluating the model's performance across different data segments and time periods to assess its robustness. Using Snowflake streams and tasks to automate the validation process on new incoming data.
• C. Relying on a simple visual inspection of model outputs and comparing them to a small sample of known fraud cases. Skipping formal validation to accelerate the deployment process.
• D. Conducting a comprehensive backtesting analysis using historical data, simulating real-world scenarios, and evaluating the model's performance under different conditions. Using Snowflake's time travel feature to access historical data snapshots for accurate backtesting. Monitoring model performance using Snowflake alerts triggered by custom SQL queries against model prediction logs.
• E. Performing a single train/test split of the historical data and evaluating model performance metrics (e.g., accuracy, precision, recall) on the test set using standard Python libraries within a Snowflake Snowpark environment. Deploying the model directly if the metrics exceed a predefined threshold.

Answer: B,D

Explanation:
Options B and C represent the most comprehensive approaches. Option B utilizes K-fold cross-validation within Snowflake for robust performance evaluation across data segments and automates validation on new data using streams and tasks. Option C emphasizes backtesting with historical data using Snowflake's time travel feature and monitors performance with alerts, ensuring real-world relevance and timely detection of performance degradation. Option A is insufficient as it relies on a single train/test split. Option D is inadequate and risky due to lack of validation. Option E is also insufficient since calculating only AUC on the entire dataset results in overfitting.

NEW QUESTION # 57
You are tasked with validating a regression model predicting customer lifetime value (CLTV). The model uses various customer attributes, including purchase history, demographics, and website activity, stored in a Snowflake table called 'CUSTOMER DATA. You want to assess the model's calibration specifically, whether the predicted CLTV values align with the actual observed CLTV values over time. Which of the following evaluation techniques would be MOST suitable for assessing the calibration of your CLTV regression model in Snowflake?

• A. Calculate the R-squared score on a hold-out test set to assess the proportion of variance in the actual CLTV explained by the model.
• B. Evaluate the model's residuals by plotting them against the predicted values and checking for patterns or heteroscedasticity.
• C. Calculate the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a hold-out test set to quantify the overall prediction accuracy.
• D. Conduct a Kolmogorov-Smirnov test to check the distribution of predicted and actual value.
• E. Create a calibration curve (also known as a reliability diagram) by binning the predicted CLTV values, calculating the average predicted CLTV and the average actual CLTV within each bin, and plotting these averages against each other.

Answer: E

Explanation:
Option B is the most suitable technique for assessing calibration. A calibration curve directly visualizes the relationship between predicted and actual values, allowing you to see if the model is systematically over- or under-predicting CLTV for different ranges of predicted values. Options A, C, and D are useful for assessing overall accuracy and model fit but do not directly address calibration. MAE and RMSE (A) measure overall error magnitude. Residual analysis (C) can reveal problems with model assumptions. R-squared (D) measures the explained variance, not calibration. Option E measures whether two samples follow the same distribution, however, it would not be most suitable for assessing calibration of your CLTV regression model.

NEW QUESTION # 58
You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

• A. Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.
• B. Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.
• C. Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
• D. Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
• E. Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.

Answer: A,C,E

Explanation:
Options B, C, and E are the correct strategies. Caching (B) reduces redundant calls. A queueing system (C) provides precise rate control but adds complexity. A retry mechanism with backoff (E) handles rate limit errors gracefully. Sleeping within a UDF (A) is inefficient and inaccurate, as it doesn't account for network latency or processing time. Scaling up the warehouse (D) might increase concurrency but won't directly address the per-second rate limit of the external API and could be cost-prohibitive.

NEW QUESTION # 59
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.
• E. 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.

Answer: C,E

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 # 60
You've created a Python UDF in Snowflake that uses the 'numpy' and libraries to perform complex statistical calculations on time-series data'. The UDF is deployed successfully, but when you execute it on a large dataset, you observe significant performance bottlenecks. Analyzing the execution plan reveals that the UDF is being executed serially for each row of the input data, preventing Snowflake from leveraging its parallel processing capabilities. What strategies can you employ to improve the performance and enable parallel execution of the UDF in Snowflake?

• A. Rewrite the UDF using Snowflake's Java UDF functionality instead of Python, as Java is inherently faster for numerical computations.
• B. Modify the UDF to accept a Pandas DataFrame as input instead of individual row values. Ensure your UDF is vectorized to process the entire DataFrame at once.
• C. Use the 'snowflake.snowpark' library to create a distributed Pandas DataFrame and perform computations directly within the Snowflake engine in a parallel manner.
• D. Increase the Snowflake warehouse size to provide more resources for serial execution.
• E. Decompose the UDF into smaller, more manageable functions and register each as a separate UDF, hoping Snowflake will parallelize the execution of these smaller UDFs automatically.

Answer: B,C

Explanation:
Options B and D are correct. Option B: Vectorizing the UDF by accepting a Pandas DataFrame allows the 'numpy' and 'scipy' operations to be applied efficiently on batches of data, leveraging the underlying parallelism of these libraries and Snowflake's engine. Option D: Using Snowpark's distributed Pandas DataFrame allows computations to be pushed down and executed in parallel within Snowflake. Option A only provides more resources but doesn't address the serial execution. Option C is not always guaranteed to be faster and introduces complexity of learning a new API. Option E doesn't guarantee that the UDFs will run in Parallel and also it increases the complexity of maintance.

NEW QUESTION # 61
You have built and deployed a model to predict the likelihood of loan default using Snowpark and deployed as a Snowflake UDF. You are using a separate Snowflake table 'LOAN APPLICATIONS' as input, which contains current applicant data'. After several weeks in production, you observe that the model's accuracy has significantly dropped. The original training data was collected during a period of low interest rates and stable economic conditions. Which of the following strategies are the MOST effective for identifying potential causes of this performance degradation and determining if a model retrain is necessary, in the context of Snowflake?

• A. Monitor the model's precision and recall using a dedicated monitoring dashboard built on top of the model's predictions and actual loan outcomes (once available). Create a Snowflake alert that triggers when either metric falls below a predefined threshold.
• B. Regularly sample data from the ' LOAN_APPLICATIONS table and manually compare it to the original training data. This provides a qualitative assessment of potential changes.
• C. Assume the model is no longer valid due to changing economic conditions and immediately retrain the model with the latest available data without further investigation.
• D. Re-run the original model training code with the 'LOAN_APPLICATIONS table as input and compare the resulting model coefficients to the coefficients of the deployed model. Significant differences indicate model decay.
• E. Compare the distribution of input features in the 'LOAN_APPLICATIONS table to the distribution of the features in the original training dataset using Snowflake's statistical functions (e.g., APPROX_COUNT DISTINCT, &AVG', 'STDDEV'). Significant deviations indicate data drift.

Answer: A,E

Explanation:
Options A and B are the most effective. A identifies data drift by comparing feature distributions, indicating potential changes in the input data. B monitors performance metrics and triggers alerts based on predefined thresholds. C is too manual and inefficient. D isn't appropriate, re-running model training would produce a new model not identifying degradation of current one. E is premature; further investigation is necessary. Assessing the performance is import after significant drops found for retraining of the model with latest data.

NEW QUESTION # 62
You are tasked with estimating the 95% confidence interval for the median annual income of Snowflake customers. Due to the non-normal distribution of incomes and a relatively small sample size (n=50), you decide to use bootstrapping. You have a Snowflake table named 'customer_income' with a column 'annual_income'. Which of the following SQL code snippets, when correctly implemented within a Python script interacting with Snowflake, would most accurately achieve this using bootstrapping with 1000 resamples and properly calculate the confidence interval?

• A.
• B.
• C.
• D.
• E.

Answer: C

Explanation:
Option A is the correct answer. It accurately implements bootstrapping by: (1) Resampling with replacement from the original data. (2) Calculating the median of each resample. (3) Computing the 2.5th and 97.5th percentiles of the bootstrap medians to obtain the 95% confidence interval. Option B calculates the mean instead of the median, and uses 'random.sample' without replacement, which is incorrect for bootstrapping. Option C doesn't resample at all, just calculates the mean of the original data repeatedly. Option D calculates the mean instead of the median. Option E calculates 90% confidence interval instead of 95%.

NEW QUESTION # 63
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. 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.
• C. 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.
• D. 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'.
• E. Increase the size of the training dataset significantly by querying data from external sources.

Answer: B,C,D

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 # 64
A data scientist is tasked with creating features for a machine learning model predicting customer churn. They have access to the following data in a Snowflake table named 'CUSTOMER ID, 'DATE, 'ACTIVITY _ TYPE' (e.g., 'login', 'purchase', 'support_ticket'), and 'ACTIVITY VALUE (e.g., amount spent, duration of login). Which of the following feature engineering strategies, leveraging Snowflake's capabilities, could be useful for predicting customer churn? (Select all that apply)

• A. Use 'APPROX COUNT DISTINCT to estimate the number of unique product categories purchased by each customer within the last 3 months to create a features.
• B. Directly use the ACTIVITY TYPE column as a categorical feature without any transformation or engineering.
• C. Calculate the recency, frequency, and monetary value (RFM) for each customer using window functions and aggregate functions.
• D. Create features that capture the trend of customer activity over time (e.g., increasing or decreasing activity) using LACY and 'LEAD' window functions.
• E. Create a feature representing the number of days since the customer's last login using "DATEDIFF and window functions.

Answer: A,C,D,E

Explanation:
Options A, B, C and D all represent valid and useful feature engineering strategies for predicting customer churn. RFM (A) is a classic approach. Calculating the days since last login (B) provides a measure of engagement. Estimating the number of unique product categories purchased (C) offers insights into customer diversity. Tracking activity trends (D) helps identify customers who are becoming less engaged. Option E would be a poor choice, as the 'ACTIVITY TYPE column, if not properly encoded, may not be effective in the machine learning model. One-hot encoding or other transformations are required for categorical features.

NEW QUESTION # 65
You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?

• A. Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
• B. Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.
• C. Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.
• D. Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.
• E. Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.

Answer: D

Explanation:
Option B is the most efficient and scalable approach. Partitioning by product category allows for faster querying of specific categories. Clustering on the timestamp column ensures that recent predictions are quickly accessible. A composite key of product category and timestamp provides uniqueness. Option A lacks any optimization for querying. Option C can lead to performance issues with large JSON documents and querying specific values within the VARIANT. Option D introduces latency due to external stage access, and external tables are generally slower for frequent queries compared to native Snowflake tables. Option E introduces external dependency and network latency, which is generally not preferred if a native Snowflake solution is possible.

NEW QUESTION # 66
You have a regression model deployed in Snowflake predicting customer churn probability, and you're using RMSE to monitor its performance. The current production RMSE is consistently higher than the RMSE you observed during initial model validation. You suspect data drift is occurring. Which of the following are effective strategies for monitoring, detecting, and mitigating this data drift to improve RMSE? (Select TWO)

• A. Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.
• B. Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.
• C. Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.
• D. Implement a process to continuously calculate and track the RMSE on a holdout dataset representing the most recent data, alerting you when the RMSE exceeds a predefined threshold.
• E. Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.

Answer: A,D

Explanation:
Option A provides a proactive approach to monitoring the model's performance on new data and triggering alerts when the RMSE deteriorates. Option C helps identify changes in the input data that could be causing the drift. Option B is not ideal, as retraining on all historical data might not effectively adapt to recent drifts. Option D is inefficient and impractical for large datasets. Option E is incorrect because a high RMSE indicates poor performance and warrants investigation, not ignoring.

NEW QUESTION # 67
You've trained a binary classification model in Snowflake to predict loan defaults. You need to understand which features are most influential in the model's predictions for individual loans. Which of the following methods provide insight into model explainability, AND how can they be leveraged within the Snowflake environment? (Select all that apply)

• A. Coefficient analysis: By inspecting the coefficients of a linear model, we can easily determine feature importances.
• B. Decision Tree visualization: Convert the model to decision trees and visualize it.
• C. LIME (Local Interpretable Model-agnostic Explanations): Can be implemented by creating a UDF (User-Defined Function) in Snowflake that takes a loan's feature values as input and returns the feature importance scores for that specific loan, based on the LIME algorithm applied to the model's predictions.
• D. SHAP (SHapley Additive explanations): Similar to LIME, SHAP values can be calculated using a Snowflake UDF, providing a more comprehensive and theoretically grounded explanation of each feature's contribution to the prediction, considering all possible feature combinations.
• E. Permutation Feature Importance: Directly supported within Snowflake ML's model evaluation functions, allowing you to rank features based on their impact on model performance when their values are randomly shuffled.

Answer: C,D

Explanation:
LIME and SHAP are valid techniques. While Snowflake ML might directly support permutation feature importance through built-in functions for model evaluation in future releases (A), currently implementing LIME or SHAP via UDFs provides granular, instance-level explainability. Coefficient analysis (D) only work for linear models, and converting an arbitrary model to decision tree (E) would result in a bad approximation.

NEW QUESTION # 68
A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

• A.
• B.
• C.
• D.
• E.

Answer: A,C

Explanation:
Options A and E are correct. Option A uses the 'scipy.stats.norm.intervar function correctly to compute the confidence interval for a proportion. Option E manually calculates the confidence interval using the standard error and the z-score for a 95% confidence level (approximately 1.96). Option B uses the t-distribution which is unnecessary for large sample sizes and is inappropriate here given the context. Option C is not the correct way to calculate the confidence interval for proportion using binomial distribution interval function, it calculates range of values in dataset, instead of confidence interval. Option D uses incorrect standard deviation.

NEW QUESTION # 69
You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The 'TRANSACTION AMOUNT' column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

• A. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT + 3 FROM TRANSACTIONS);
• B. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION AMOUNT > q3 + (1.5 iqr);
• C. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT < qi - (1.5 iqr);
• D. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT MEDIAN(TRANSACTION AMOUNT) FROM TRANSACTIONS);
• E. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT WITHIN GROUP (ORDER BY TRANSACTION_AMOUNT) FROM TRANSACTIONS);

Answer: B

Explanation:
Option B correctly implements the IQR method for identifying outliers. The formula 'Q3 + (1.5 identifies values significantly higher than the third quartile, which is appropriate for detecting outliers in a right-skewed distribution. Options A uses standard deviation, which is less robust to outliers. Option C finds the 95th percentile but might not isolate extreme outliers. Option D looks for lower bound outliers, which is not the case. Option E uses the median which not effective to find out outlier values.

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