[Q43-Q61] Try DSA-C03 Free Now! Real Exam Question Answers Updated [Mar 10, 2026]

Try DSA-C03 Free Now! Real Exam Question Answers Updated [Mar 10, 2026]

Get Ready to Pass the DSA-C03 exam with Snowflake Latest Practice Exam 

NEW QUESTION # 43
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. 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.
• B. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
• 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 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'.
• E. 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.

Answer: D,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 # 44
You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

Answer: C

Explanation:
Option C is the most secure and efficient. It correctly uses the 'API_INTEGRATION' to leverage Snowflake's security features for managing credentials. It also includes the 'HEADERS' parameter to specify the content type, which is essential for proper communication with the SageMaker endpoint. REQUEST and RESPONSE translators can avoid some of the boilerplate JSON building and parsing within the Snowflake environment. The other options are either missing crucial headers or do not use the API integration securely.

NEW QUESTION # 45
You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

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

Answer: E

Explanation:
Option E is the most efficient because it leverages Snowpark's built-in functions for string manipulation and conditional logic directly. It first removes all non-numeric characters using 'regexp_replace' and then uses 'iff (if and only if) to replace empty strings (resulting from cleaning) with NULL. This approach avoids using UDFs (User-Defined Functions), which can introduce overhead. Option B, although using 'regexp_replace' , requires an additional 'with_column' to handle empty strings after cleaning. Option A introduces UDF that decreases performance. Option C calls UDF with undefined 'call_udf function and 'snowflake-snowpark-python' library. Option D is missing dataframe and its transformation is not happening on top of Dataframe. Option E is preferrable over Option B, as it uses the single transformation.

NEW QUESTION # 46
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. 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.
• 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. 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.
• D. 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.
• E. 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).

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 # 47
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. 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.
• B. Disable the model and revert to a rule-based system, abandoning the machine learning approach altogether.
• 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. Adjust the existing model's parameters manually to compensate for the observed performance degradation without retraining or versioning.
• E. 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.

Answer: E

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 # 48
You are developing a machine learning model within a Snowflake UDF (User-Defined Function) written in Python. This UDF needs to access external Python libraries not included in the default Snowflake Anaconda channel. You've created a stage and uploaded the necessary file. You've successfully used 'conda create' and 'conda install --file requirements.txt' to create your environment locally, and subsequently zipped the environment. Now, what steps are essential to configure the Snowflake UDF to correctly use these external libraries from the stage? Select all that apply.

• A. Set the 'PYTHON_VERSION' parameter of the 'CREATE OR REPLACE FUNCTION' statement to match the Python version used in your environment using e.g. 'PYTHON_VERSION = '3.8".
• B. Create a ZIP file containing the Python environment and upload it to a Snowflake stage.
• C. Include the line 'import sys; sys._xoptions['snowflake_home'] = at the top of your UDF to point to the environment stage location.
• D. Install the packages directly into the Snowflake environment using 'CREATE OR REPLACE FUNCTION RETURNS VARCHAR ..: and a pip install command within the function.
• E. Specify the stage path containing the zipped environment in the 'imports' clause of the 'CREATE OR REPLACE FUNCTION' statement using the symbol and specifying the zip file e.g., '@snowflake_packages/myenv.zip'.

Answer: A,B,E

Explanation:
Options B, C, and D are crucial. Snowflake UDFs can use custom environments created and uploaded as ZIP files to a stage. The 'imports' clause in the function definition must point to the ZIP file on the stage (Option C). The 'PYTHON_VERSION' must match the environment's Python version (Option D). Option B describes the process of creating a deployment-ready ZIP file. Option A's approach of manually setting 'sys._xoptions' is incorrect and not a recommended or supported method. Option E is not the standard way to manage external libraries; uploading a pre-built environment is more reliable and avoids dependency conflicts during UDF execution.

NEW QUESTION # 49
You've deployed a fraud detection model in Snowflake using Snowpark. You are monitoring its performance and notice a significant decrease in recall, while precision remains high. This means the model is missing many fraudulent transactions. The training data was initially balanced, but you suspect that recent changes in user behavior have skewed the distribution of fraudulent vs. non-fraudulent transactions in production. Which of the following actions are MOST appropriate to address this issue and improve the model's performance, considering best practices for model retraining within the Snowflake ecosystem?

• A. Immediately shut down the model to prevent further inaccurate classifications. Investigate why the recall is low before any retraining is performed.
• B. Retrain the model using the original training data. Since the precision is high, the model's fundamental logic is still sound. A larger training dataset isn't necessary.
• C. Retrain the model using a dataset that includes recent production data, being sure to re-balance the dataset to maintain a roughly equal number of fraudulent and non-fraudulent transactions. Prioritize transactions from the last month.
• D. Implement a data drift monitoring system in Snowflake to automatically detect changes in the input features of the model. Trigger an automated retraining pipeline when significant drift is detected. This retraining should include recent production data with updated labels, but only if label data collection can be automated.
• E. Adjust the model's classification threshold to be more sensitive, even if it means accepting a slightly lower precision. This can be done directly within Snowflake using a SQL UDF that transforms the model's output probabilities.

Answer: C,D,E

Explanation:
Options B, C, and D are the most appropriate. B addresses the data drift by incorporating recent production data with re-balancing to mitigate the skewed distribution. C directly improves recall by adjusting the classification threshold. D establishes a proactive drift detection and retraining system which is a best practice for long-term model maintenance. A is incorrect because the original data doesn't reflect current trends. E is too drastic initially; adjusting the threshold and retraining are preferred first. Retraining with balanced, recent data is critical, especially if the class distribution has shifted. Monitoring for drift provides an automated approach to maintaining model accuracy in a changing environment. Also a low code retraining pipeline is appropriate considering current model performance with SQL udf transformations.

NEW QUESTION # 50
You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?

• A. Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.
• B. Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.
• C. Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
• D. Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.
• E. Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.

Answer: B

Explanation:
The most appropriate approach is to extract the data and perform the confidence interval calculations outside of the stored procedure using a dedicated statistical environment. Options A and D are less scalable and efficient within the stored procedure. Option B provides insufficient information. Option E is not feasible for dynamic calculation based on changing data.

NEW QUESTION # 51
You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

• A. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
• B. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
• C. Create an associated Stream on the source tables used for Feature Groups
• D. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
• E. Explicitly specify a in the call.

Answer: A,D

Explanation:
Options B and C are correct. B: Specifying a 'timestamp_key' during Feature Group creation is crucial for enabling point-in-time correctness. This tells the Feature Store which column represents the event timestamp. C: The method is specifically designed for point-in-time lookups. It requires a dataframe containing primary keys and the desired timestamp for each lookup. This enables the Feature Store to retrieve the feature values as they were at that specific point in time. Option A is incorrect, while enabling CDC is valuable for incremental updates, it does not guarantee point-in-time correctness without specifying the timestamp key and retrieving historical features using that key. Option D is not necessary, streams enable incremental loads but are separate from point in time. Option E, is not needed, its implicit via using .

NEW QUESTION # 52
You are tasked with performing data profiling on a large customer dataset in Snowflake to identify potential issues with data quality and discover initial patterns. The dataset contains personally identifiable information (PII). Which of the following Snowpark and SQL techniques would be most appropriate to perform this task while minimizing the risk of exposing sensitive data during the exploratory data analysis phase?

• A. Utilize Snowpark to create a sampled dataset (e.g., 1% of the original data) and perform all exploratory data analysis on the sample to reduce the data volume and potential exposure of PII.
• B. Create a masked view of the customer data using Snowflake's dynamic data masking features. This view masks sensitive PII columns while allowing you to compute aggregate statistics and identify patterns using SQL and Snowpark functions. Columns like 'email' are masked using and columns like are masked using .
• C. Apply differential privacy techniques using Snowpark to add noise to the summary statistics generated from the customer data, masking the individual contributions of each customer while revealing overall trends.
• D. Directly query the raw customer data using SQL and Snowpark, computing descriptive statistics like mean, median, and standard deviation for all numeric columns and frequency counts for categorical columns. Store the results in a temporary table for further analysis.
• E. Export the entire customer dataset to an external data lake for exploratory analysis using Spark and Python. Apply data masking in Spark before analysis.

Answer: B,C

Explanation:
Options C and D provide the most secure and effective ways to perform exploratory data analysis while protecting PII. Differential privacy (C) ensures that aggregate statistics do not reveal too much information about individuals. Masked views (D) prevent direct access to sensitive data, replacing it with masked values during the analysis. A is dangerous because it exposes the raw data. B while reduces the volume, still exposes raw data. E is risky because it involves exporting sensitive data outside of Snowflake.

NEW QUESTION # 53
You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

• A. Construct a stored procedure that uses the available aggregated statistics (e.g., mean, standard deviation, and sample size) to generate bootstrap samples based on an assumed parametric distribution (e.g., gamma or log-normal) fitted to the data, and then estimate the standard error from these resamples.
• B. Use the available aggregate statistics to create many synthetic datasets, all adhering to the same mean, variance, and total sample size. Then, compute the statistic of interest (mean transaction amount) for each of these synthetic datasets, and use this collection to estimate the standard error. This is a valid approach.
• C. Even without individual transaction data, bootstrapping is fundamentally impossible in this scenario, as bootstrapping requires resampling from the original data . All given options are therefore equally inappropriate.
• D. Develop a stored procedure that generates random samples from a normal distribution with the same mean and standard deviation as the aggregated transaction data available to you, then calculates the standard error of the mean from these synthetic resamples.
• E. Attempt to apply the central limit theorem rather than bootstrapping.

Answer: D

Explanation:
Option A is the least appropriate. Generating random samples from a normal distribution with the same mean and standard deviation as the aggregated data, fundamentally violates the principle of bootstrapping. Bootstrapping relies on resampling from the original data to approximate the sampling distribution of a statistic. Creating data from a pre-defined distribution removes the inherent characteristics of the true data generating process and produces potentially very misleading results. Option B, using a parametric distribution, while still based on assumptions, is slightly better than A as it attempts to fit a distribution to the known data characteristics, but still relies on potentially incorrect distribution assumptions. Option C is not correct. Even the most inappropriate usage will give an answer. Option D is a valid approach, but it not Bootstrapping. Option E follows the basic idea of bootstrapping.

NEW QUESTION # 54
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. 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.
• 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. 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.
• D. 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.
• E. 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.

Answer: B,E

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 # 55
You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

• A. Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.
• B. Create a materialized view containing pre-computed embeddings for all transaction descriptions.
• C. Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model
• D. Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
• E. Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.

Answer: A,D

Explanation:
Option B is a better approach compared to option A to generate embeddings because its incrementally generate embeddings for new transactions. Option E is also an important approach where if transaction description remains same for the embeddings will not be re-computed. Materialized view is not suited for API integrations like those using Snowflake Cortex. Option D is technically correct, but doesn't address the optimization and cost concerns. Option A Regenerating embeddings for the entire dataset daily is computationally expensive and can quickly lead to high costs, especially with Snowflake Cortex. The best approach is to use caching and compute only for a new transaction description. So correct answer is B and E.

NEW QUESTION # 56
You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)

• A. Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
• B. Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.
• C. Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.
• D. Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
• E. Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.

Answer: C,D,E

Explanation:
Options A, B, and E are valid and efficient approaches. Option A using 'NTILE' is a direct and efficient way to create quantile bins within Snowflake SQL, and can find the most recent order date for customer with a case statement. Option B calculates the percentiles directly and then uses a CASE statement to assign bins. This is also efficient for explicit boundaries. Option E finds the boundaries of the quantile using 'APPROX_PERCENTILE or 'PERCENTILE_CONT , after that you can use 'WIDTH_BUCKET to categorize into quantile bins based on ranges. Option C is possible but generally less efficient due to the overhead of UDF execution and data transfer between Snowflake and the UDF environment. Option D is valid, but creating a temporary table adds complexity and potentially reduces performance compared to window functions or direct quantile calculation within the query.

NEW QUESTION # 57
You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?

• A.
• B.
• C.
• D. None of the above
• E.

Answer: E

Explanation:
Option A correctly addresses all requirements: Filters usage data for the last 30 days. Corrects negative values by setting them to 0 using and ' Calculates the sum of for each customer. Uses a 'LEFT JOIN' to include all customers, even those without recent usage data. Uses 'coalesce()' to set the to 0 for customers with no usage data after the join. Option B uses an ' INNER JOIN' , which would exclude customers without any recent usage data, violating the requirement to include all customers. Option C does not treat negative usage values correctly. Option D uses a "RIGHT JOIN' which would return incorrect results. Option E isn't right as option A correctly addresses all the scenarios.

NEW QUESTION # 58
You are developing a model to predict house prices based on structured data including size, number of bedrooms, location, and age. You have built a linear regression model within Snowflake. During the evaluation, you observe that the residuals exhibit heteroscedasticity. Which of the following actions is the LEAST appropriate to address heteroscedasticity in this scenario, considering you want to implement the solution primarily using Snowflake's built-in features and capabilities?

• A. Include interaction terms between the independent variables in your linear regression model.
• B. Implement Weighted Least Squares (WLS) regression by calculating weights inversely proportional to the variance of the residuals for each data point. This involves creating a UDF to calculate weights and modifying the linear regression model fitting process. (Assume direct modification of the fitting process is possible within Snowflake).
• C. Use robust standard errors in the linear regression analysis, even though Snowflake doesn't directly support calculating them. You decide to export model coefficients to an external statistics package (e.g., Python with Statsmodels) to compute robust standard errors and then bring insights back to Snowflake.
• D. Apply a logarithmic transformation to the target variable ('SALES_PRICE) using the 'LOG' function within Snowflake before training the linear regression model.
• E. Transform independent variables using Box-Cox transformation and include in Snowflake Linear Regression Model Training

Answer: C

Explanation:
Option C is the least appropriate because it requires exporting model coefficients to an external tool to calculate robust standard errors. While robust standard errors are a valid way to address heteroscedasticity's impact on inference (hypothesis testing), the question explicitly prioritizes using Snowflake's built-in capabilities. Options A, B, D and E involve transformations/modifications within Snowflake itself. Applying a logarithmic transformation (A) can stabilize variance. Implementing WLS (B) directly addresses the unequal variances. Including interaction terms (D) can capture non-linear relationships and address some heteroscedasticity. Box-Cox Transformation (E) is a general way to transform non-normal independent variables.

NEW QUESTION # 59
You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

• A. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
• B. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
• C. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
• D. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
• E. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.

Answer: E

Explanation:
Option E correctly identifies the most appropriate metrics (Precision, Recall, Fl-score, AUC-ROC, and Log Loss) for evaluating a binary classification model, especially in the context of imbalanced datasets. It also correctly describes the focus of each metric. Accuracy can be misleading with imbalanced datasets. MSE and R-squared are for regression problems (Option B). Confusion Matrix is a table, and Options D, contains incorrect statement.

NEW QUESTION # 60
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

• A. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
• B. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
• C. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
• D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
• E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.

Answer: E

Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.

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