[Sep-2025] Get 100% Real DSA-C03 Free Online Practice Test [Q63-Q85]

[Sep-2025] Get 100% Real DSA-C03 Free Online Practice Test

BEST Verified Snowflake DSA-C03 Exam Questions (2025)

NEW QUESTION # 63
You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?

Answer: C

Explanation:
Option B is the most correct. It correctly calculates the median and mean for the specified customer segments using 'agg()' with .alias(y to name the resulting aggregate columns, and then retrieves the values using . This approach correctly handles the aggregation and retrieval of the calculated median and mean values. Option A uses which although technically works, is less readable than the aliased approach. The method provides similar performance benefits to the method with simpler syntax, as you retrieve only the first row of the DataFrame. 'toLocallterator' is a performant way to get local access to the result of an aggregation function when a small number of rows are expected. Option C fails because it attempts to use the aggregate directly without materializing the value. The comparison between using .agg(), .collect(), .first(), and .toLocallterator() demonstrates performance tuning knowledge.

NEW QUESTION # 64
You are tasked with identifying fraudulent transactions from unstructured log data stored in Snowflake. The logs contain various fields, including timestamps, user IDs, and transaction details embedded within free-text descriptions. You plan to use a supervised learning approach, having labeled a subset of transactions as 'fraudulent' or 'not fraudulent.' Which of the following methods best describes the extraction and processing of this data for training a machine learning model within Snowflake?

A. Use regular expressions within a Snowflake UDF to extract relevant information (e.g., amount, item description) from the log descriptions. Convert extracted data into numerical features using one-hot encoding within the UDF. Then, train a model using the extracted numerical features directly within Snowflake using SQL extensions for machine learning.
B. Export the entire log data to an external machine learning platform (e.g., AWS SageMaker) and perform feature extraction, NLP processing, and model training there. Import the trained model back into Snowflake as a UDF for prediction.
C. Extract the entire log description field and train a word embedding model (e.g., Word2Vec) on the entire dataset. Average the word vectors for each transaction's log description to create a document vector. Train a classification model (e.g., Random Forest) on these document vectors within Snowflake.
D. Treat the unstructured log description as a categorical feature and directly apply one-hot encoding within Snowflake, then train a classification model. Due to high dimensionality perform PCA for dimensionality reduction before training.
E. Use a combination of regular expressions and natural language processing (NLP) techniques within Snowflake UDFs to extract key features such as transaction amounts, product categories, and sentiment scores from the log descriptions. Then, combine these extracted features with other structured data (e.g., user demographics) and train a classification model using these features. The NLP steps include tokenization, stop word removal, and TF-IDF vectorization.

Answer: E

Explanation:
Option C provides the most comprehensive and effective approach. It combines the strengths of both regular expressions (for structured data extraction) and NLP techniques (for understanding the semantic content of the log descriptions). Using Snowflake UDFs keeps the data processing within Snowflake, minimizing data movement. Combining extracted features with other structured data enhances the model's performance.

NEW QUESTION # 65
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. 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.
B. 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.
C. Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.
D. 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.
E. Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.

Answer: D

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 # 66
You are tasked with training a model within Snowflake to predict customer churn for a telecommunications company. The dataset is stored in a Snowflake table named 'CUSTOMER DATA. The features include 'age', and 'data_usage'. The target variable is 'churned' (boolean). You want to use the SNOWFLAKE.ML.ANACONDA INTEGRATION to leverage Scikit-learn for model training. Which of the following code snippets correctly performs model training with Snowflake ML, addressing potential issues like feature scaling and data type handling within the stored procedure?

Answer: D

Explanation:
Option D correctly implements the model training procedure within Snowflake. It includes: 1. Necessary packages: 'scikit-learn' , pandaS and 'joblib' are included. 2. Feature scaling: 'StandardScaler' is used to scale the features, which is important for logistic regression. 3. Data handling: 'pd.read_sqr is used to fetch data into a pandas DataFrame. Explicit Snowflake connection function is created and handles exceptiom 4. Correct Model Persistence: 'joblib.dump' is used to persist the model. Option A is missing the explicit Snowflake Connection creation, which is needed. Option B, doesn't include the feature scaling. Option C, doesn't include packages and tries to perform split on SQL statement directly which is incorrect. Option D, doesn't transform the test data as part of feature scaling, also X is incorrectly assigned in-place to the fitted scaler.

NEW QUESTION # 67
You've trained a sentiment analysis model in Snowflake using Snowpark Python and deployed it as a UDF. After several weeks, you notice the model's performance has degraded significantly. You suspect concept drift. Which of the following actions represent the MOST effective and comprehensive approach to address this situation, considering the entire Machine Learning Lifecycle, including monitoring, retraining, and model versioning? Assume you have monitoring in place that alerted you to the drift.

A. Analyze the recent data to understand the nature of the concept drift, retrain the model with a combination of historical and recent data, version the new model, and perform AIB testing against the existing model before fully deploying the new version. Log both model version predictions during AIB testing.
B. Adjust the existing model's parameters manually to compensate for the observed performance degradation without retraining or versioning.
C. Retrain the model on a sample of the most recent data, overwriting the original model files in your Snowflake stage and updating the UDF definition. Keep no record of the old model.
D. Disable the model and revert to a rule-based system, abandoning the machine learning approach altogether.
E. Immediately replace the current UDF with a newly trained model using the latest data, ignoring model versioning and assuming the latest data will solve the drift issue.

Answer: A

Explanation:
Addressing concept drift requires careful analysis, retraining with appropriate data (historical + recent), and controlled deployment using A/B testing. Model versioning ensures that you can rollback if the new model performs poorly. Logging the predictions of both models assists in further performance analysis. Directly replacing (option A) or manually adjusting (option C) are risky without proper evaluation. Abandoning the ML approach (option D) is a last resort. Option E lacks model versioning and thus risks complete loss of the older model which is a common practice violation in ML engineering.

NEW QUESTION # 68
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. Evaluate the model's residuals by plotting them against the predicted values and checking for patterns or heteroscedasticity.
B. 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.
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. 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.
E. Conduct a Kolmogorov-Smirnov test to check the distribution of predicted and actual value.

Answer: B

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 # 69
A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

A. The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.
B. Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
C. The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
D. The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
E. Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.

Answer: A,D

Explanation:
Options B and C are correct. Bootstrapping relies on the assumption that the original sample is representative of the population. If it isn't, the bootstrap distribution will reflect the biases of the sample. Also certain statistics, particularly those sensitive to outliers or with high variance, can produce bootstrap distributions that differ significantly from the original data distribution. Option A is incorrect because the bootstrap distribution doesn't necessarily have to be same as sample distribution. Option D is incorrect since Bootstrapping makes no assumptions regarding the distribution of original dataset and can be used for any data distribution. Option E is not correct. Bootstrapping is not always accurate and relies on assumptions to perform correctly.

NEW QUESTION # 70
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 A
B. Option D
C. Option C
D. Option E
E. Option B

Answer: D

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

NEW QUESTION # 71
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 A
B. Option D
C. Option C
D. Option E
E. Option B

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 # 72
You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

A. Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
B. Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.
C. Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
D. Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
E. Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.

Answer: A,D

Explanation:
Options A and C are correct. Increasing the warehouse size provides more compute resources, leading to faster execution. Vectorized UDFs (especially Pandas UDFs for Python-based models) are highly efficient for batch processing, as they leverage Snowflake's parallel processing capabilities. B is incorrect as Snowpark UDFs are often more efficient due to their ability to use compiled languages and optimized libraries. Result caching (Option D) might help if the same input data is frequently used, but it won't improve the performance for new data. Row-level security (Option E) is primarily for security and won't directly improve UDF performance in this context.

NEW QUESTION # 73
You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?

A. Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
B. Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
C. Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.
D. Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
E. Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.

Answer: A,E

Explanation:
Option B is the most comprehensive. It establishes a system for continuous monitoring of model performance using real-world feedback, and alerts you when performance degrades. Option E is also strong because it allows for direct comparison of a new model against the existing model in a production setting, identifying model decay before it significantly impacts performance. Options A and D are insufficient for monitoring as they lack real-world feedback loops for continuous assessment. Simply retraininig frequently does not guarantee model improvements, and option C relies on manual intervention and lacks granular monitoring of the model's specific performance. Shadow Deployment is costly but more robust.

NEW QUESTION # 74
You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named 'SALES DATA', with columns 'SALE DATE (DATE type) and 'SALES AMOUNT' (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named 'ds' (for dates) and 'y' (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you've already created the Python UDF 'prophet_forecast'?

Answer: B

Explanation:
The correct approach is to construct JSON objects with 'ds' and 'y' as keys and the corresponding 'SALE_DATE and 'SALES_AMOUNT as values. Then, these JSON objects are aggregated into an array using ARRAY AGG(). This array is then passed to the prophet_forecast' UDF. Options A, B, and D are incorrect because they either pass individual dates and sales amounts as separate arrays or pass the JSON object one by one which is not the desired approach for Prophet UDE.

NEW QUESTION # 75
You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A. Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.
B. Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
C. Revert to a previous version of the model known to have performed well, while investigating the issue in the background.
D. Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
E. Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.

Answer: D

Explanation:
Option D is the best first step. Data quality monitoring and PSI allow for quantifying and identifying data drift. SHAP (B) is useful after determining that concept drift is the problem. Retraining immediately (A) without understanding the cause can exacerbate the problem. Reverting (C) is a temporary fix, not a solution. Adjusting the threshold (E) without understanding the underlying issue is also not a proper diagnostic approach.

NEW QUESTION # 76
You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A. 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').
B. 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.
C. 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
D. 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
E. 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.

Answer: D,E

Explanation:
Options B and E are the MOST effective because they incorporate time-based features and indicators of engagement trends. Recency (days since last order) captures the time elapsed since the customer's last interaction. Calculating changes in page views, the number of login days and linear regression slope identifies trends in engagement. Rolling averages smooth out daily fluctuations and capture longer-term patterns. Standard deviation of page views indicates a trend in page view variance, and thus overall customer engagement variance. Option A lacks recency and trend information. Option C misses temporal analysis. Option D has less relevance features and can be used however it is more useful to compare how well a customer is engaged with previous activity.

NEW QUESTION # 77
You are analyzing customer churn for a telecommunications company. You have a Snowflake table called 'CUSTOMER ACTIVITY with columns 'CUSTOMER ID', 'CALL DURATION_SUM' (total call duration in minutes), 'DATA USAGE GB' (total data usage in GB), 'CONTRACT LENGTH MONTHS', and 'CHURNED' (boolean indicating whether the customer churned). You want to understand the relationship between these features and churn. Specifically, you want to visualize the distribution of 'CALL DURATION SUM' for churned and non-churned customers. Which of the following visualizations, combined with appropriate Snowflake SQL to prepare the data, would BEST illustrate the relationship between 'CALL DURATION SUM' and 'CHURNED'?

A. A line chart plotting the average 'CALL DURATION SUM' over time, ignoring the 'CHURNED' status.
B. A pie chart showing the percentage of churned and non-churned customers, with no consideration of 'CALL DURATION SUM'
C. A histogram of 'CALL DURATION SUM" for churned customers and a separate histogram of "CALL DURATION SUM' for non-churned customers, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.
D. A scatter plot with on the x-axis and 'CHURNED' (0 or 1) on the y-axis, generated directly from the table using an external visualization tool connected to Snowflake.
E. A box plot with 'CHURNED on the x-axis and "CALL DURATION SUM' on the y-axis, generated using an external visualization tool connected to Snowflake, after preparing the data using a CTE (Common Table Expression) in Snowflake to categorize customers by churn status.

Answer: E

Explanation:
Option C is the best choice- A box plot effectively visualizes the distribution of ' CALL DURATION SUM' for each 'CHURNED' category (churned and non-churned). It shows the median, quartiles, and outliers, allowing for a clear comparison of the distribution of call durations between the two groups. The CTE allows for any required aggregation or filtering before sending the data to the visualization tool- A scatter plot (option A) is not ideal for visualizing distributions. Histograms (option B) can work, but box plots are often more concise and informative for comparing distributions across groups. A pie chart (option D) ignores 'CALL DURATION SUM'- Aline chart (option E) ignores individual customers and time, losing the ability to relate 'CALL DURATION SUM' and 'CHURNED' at the customer level.

NEW QUESTION # 78
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 E
C. Option C
D. Option D
E. Option B

Answer: D

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 # 79
You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A. AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.
B. Recall, optimized with a threshold adjustment to minimize false negatives.
C. F 1-Score, optimized to balance precision and recall equally.
D. Precision, optimized with a threshold adjustment to minimize false positives.
E. Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.

Answer: D

Explanation:
Precision is the most suitable optimization metric because it focuses on minimizing false positives. In fraud detection, incorrectly flagging legitimate transactions as fraudulent can have significant negative consequences for customers and the business. By optimizing for precision and adjusting the prediction threshold to further minimize false positives, you can ensure that the model identifies fraudulent transactions with a high degree of certainty. Recall would prioritize catching all fraud cases, even at the cost of increased false positives, which is not desirable in this scenario. While F1 balances precision and recall, the scenario specifically prioritizes precision. AUC-ROC is a good general measure of performance but does not directly address the specific requirement of minimizing false positives.

NEW QUESTION # 80
You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?

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

Answer: A,E

Explanation:
Options B and D are correct because they employ techniques to mitigate overfitting. Option B uses ' RandomizedSearchCV' with cross-validation and a fixed 'random_state' , making the search reproducible and preventing overfitting by evaluating performance on multiple validation sets. Option D leverages 'BayesianSearchCV' , which uses a probabilistic model to efficiently explore the hyperparameter space, also with cross-validation and a fixed random state making search reproducible. Both methods aim to find a balance between model complexity and generalization ability. Option A is incorrect because it does not use cross-validation, which is crucial for preventing overfitting. Option C is incorrect because manual tuning without a systematic search and cross-validation is prone to bias and overfitting. Finally, option E is incorrect because while using a modern algorithm, it lacks a random state, making it difficult to reproduce the outcome.

NEW QUESTION # 81
You have a structured dataset in Snowflake containing customer information and purchase history. You aim to build a multi-class classification model to predict customer churn, categorizing customers into 'Low Risk', 'Medium Risk', and 'High Risk' of churning. After training the model, you want to evaluate its performance. Which of the following metrics and evaluation techniques, when used together, provide the MOST comprehensive understanding of the model's performance across all churn risk categories, especially when dealing with potential class imbalance?

A. Only Overall Accuracy and a confusion Matrix.
B. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (Coefficient of Determination).
C. Area Under the ROC Curve (AUC-ROC) for each class (one-vs-rest approach), Precision-Recall Curve for each class, and Cumulative Accuracy Profile (CAP) curve.
D. Log Loss (Cross-Entropy Loss), Gini Coefficient, and Kolmogorov-Smirnov (KS) statistic.
E. Overall Accuracy, Precision, Recall, F I-Score for each class, and Confusion Matrix.

Answer: E

Explanation:
Option A offers the most comprehensive evaluation. Overall accuracy provides a general sense of performance, but can be misleading with imbalanced classes. Precision, recall, and Fl-score, calculated for each class, give a detailed view of the model's performance on each churn risk category. The confusion matrix provides a visual representation of the model's classification errors, allowing you to identify patterns of misclassification between the different risk levels. Option B, ROC AUC and Precision-Recall curve are also relevant but is better for binary classification (with one-vs-rest extended for multiclass). CAP curves are less common. Option C (Log Loss, Gini, KS) is more suitable for binary classification or ranking problems. Option D (RMSE, MAE, R-squared) are regression metrics, not suitable for classification.

NEW QUESTION # 82
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 A
D. Option D
E. Option E

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 # 83
You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

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

Answer: D

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 # 84
You have successfully deployed a real-time prediction service using Snowpark Container Services, consuming events from a Kafka topic. The service leverages a large language model (LLM) stored in the Snowflake Model Registry. You observe that inference latency is high and the service is struggling to keep up with the incoming event rate. You need to optimize the service for higher throughput and lower latency. Which of the following actions, when implemented together, would most effectively improve the performance of your Snowpark Container Services deployment?

A. Increase the 'container.resources.memory' allocation for the service. Implement caching of frequently accessed data within the containerized application.
B. Enable autoscaling for the service based on CPU utilization. Remove all logging statements from the containerized application to reduce 1/0 overhead.
C. Switch to a smaller, less accurate LLM. Increase the 'container.resources.cpu' allocation for the service. Ensure data is pre-processed before sending to kafka.
D. Increase the number of replicas for the service. Implement batching within the containerized application to process multiple events in a single inference call.
E. Implement custom monitoring solution outside of snowflake and determine bottleneck of your application. Increase the container.resources.gpu allocation for the service.

Answer: A,D

Explanation:
Options A and D, when combined, offer the most effective approach for improving throughput and reducing latency. Increasing the number of replicas allows for parallel processing of incoming events, distributing the load across multiple containers. Batching reduces the overhead of individual inference calls by processing multiple events together, improving overall throughput. Increasing the memory allocation allows the container to handle larger batches and cache more data. Implementing caching will reduce the number of times your container will pull the model, hence increasing the overall throughput. Option B might improve latency, but at the cost of accuracy. Increasing CPU allocation alone may not be sufficient if the bottleneck is memory or 1/0. Preprocessing data before sending to kafka is a good practice but it doesn't specifically impact the container performance. Option C Autoscaling is beneficial, but it won't address the underlying issue of inefficient inference. Removing logging statements might offer a minor performance improvement, but it's unlikely to be a significant factor. Option E While monitoring is important, it doesn't directly address the performance bottleneck. Also increasing gpu may not solve the problem.

NEW QUESTION # 85
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