[2025年08月最新リリース]DSA-C03試験問題はあなたをパスさせる [Q54-Q71]

質問 54
A financial institution wants to predict fraudulent transactions on credit card data stored in Snowflake. The dataset includes features like transaction amount, merchant ID, location, time of day, and user profile information. The target variable is ‘is_fraudulent’ (0 or 1). You have trained several binary classification models (Logistic Regression, Random Forest, and Gradient Boosting) using scikit-learn and persisted them using a Snowflake external function for inference. To optimize for both performance (inference speed) and accuracy, which of the following steps should you consider before deploying your model for real-time scoring using the external function? SELECT ALL THAT APPLY.

Implement feature selection techniques (e.g., using feature importance scores from Random Forest or Gradient Boosting) to reduce the number of features passed to the external function, improving inference speed.

Increase the batch size of requests sent to the external function to amortize the overhead of invoking the external function itself, even if it increases latency for individual transactions.

Normalize or standardize the input features in Snowflake using SQL before passing them to the external function to ensure consistent scaling and potentially improve model performance.

Evaluate the models on a representative held-out dataset within Snowflake using SQL queries (e.g., calculating AUC, precision, recall) to choose the model with the best balance of performance and accuracy before deploying it.

Replace the trained models with a simple rule-based system based solely on transaction amount. If the amount is greater than a threshold, flag it as fraudulent, as this will be faster than calling the external function.

質問 55
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?

Option A

Option B

Option C

Option D

Option E

質問 56
Consider the following Snowflake SQL query used to calculate the RMSE for a regression model’s predictions, where ‘actual_value’ is the actual value and ‘predicted value’ is the model’s prediction. However, you notice that the RMSE calculation is incorrect due to an error in the query. Identify the error in the query and provide the corrected query. The table name is ‘sales_predictions’.

Which of the following options represents the corrected query that accurately calculates the RMSE?

質問 57
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?

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.

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.

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.

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.

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.

質問 58
A telecom company, ‘ConnectPlus’, observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

The CLT is not applicable because the population distribution (call durations) is heavily skewed.

The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.

The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution’s heavy tail.

The CLT is applicable, and the sample mean will converge to the population median.

The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.

質問 59
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?

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.

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.

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.

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.

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.

質問 60
You’re building a regression model using Snowpark Python to predict house prices. After initial training, you observe that the model consistently overestimates the prices of high-value houses and underestimates the prices of low-value houses. Given the options below, which optimization metric, along with code snippet to calculate it using Snowpark, would be most effective in addressing this specific issue?

Mean Absolute Error MAE – as it is sensitive to outliers and will penalize large errors more heavily.

Root Mean Squared Error (RMSE) – as it gives more weight to larger errors, making it suitable for addressing the underestimation/overestimation problem.

R-squared – as it measures the proportion of variance explained, directly addressing how well the model fits the data across all price ranges.

Mean Squared Error (MSE) – as it is less sensitive to outliers than RMSE.

Adjusted R-squared – as it penalizes the addition of irrelevant features, improving the model’s generalization ability.

質問 61
You are training a regression model to predict house prices using a Snowflake dataset. The dataset contains various features, including ‘number of_bedrooms’, , and You want to use time-based partitioning for your training, validation, and holdout sets. However, you also need to ensure that the dataset is properly shuffled within each time partition to mitigate potential bias introduced by the order of data entry. Which of the following strategies is MOST EFFECTIVE and EFFICIENT for partitioning your data into train, validation, and holdout sets in Snowflake, while also ensuring random shuffling within each partition, and addressing potential data leakage issues?

Create separate views for train, validation, and holdout sets, filtering by ‘sale_date’ . Shuffle the entire dataset using ‘ORDER BY RANDOM()’ before creating the views to ensure randomness across all sets. This does not address shuffling within parition.

Create a new column ‘split_group’ using a CASE statement based on ‘sale_date’ to assign each row to ‘train’, ‘validation’, or ‘holdout’. Then, create temporary tables for each split using ‘CREATE TABLE AS SELECT FROM WHERE split_group = ORDER BY RANDOM()’. This can be very slow because of global RANDOM sort and leakage issues with using full dataset for randomness.

Use Snowflake’s SAMPLE clause with a ‘REPEATABLE seed for each split (train, validation, holdout), filtering by ‘sale_date’. Add an ‘ORDER BY RANDOM()’ clause within each ‘SAMPLE query to shuffle the data within each split. This approach does not guarantee non-overlapping sets and can introduce sampling bias.

Create a user-defined function (UDF) in Python that takes a ‘sale_date’ as input and returns either ‘train’, ‘validation’, or ‘holdout’ based on pre-defined date ranges. Apply this UDF to each row, creating a ‘split_group’ column. Then, create temporary tables for each split using ‘CREATE TABLE AS SELECT … FROM . WHERE split_group = … ORDER BY RANDOM()’. UDF overhead and global RANDOM sort make it very slow.

Create a new column ‘split_group’ using a CASE statement based on ‘sale_date’ to assign each row to ‘train’, ‘validation’, or ‘holdout’. Calculate a random number within each ‘split_group’ by using OVER (PARTITION BY split_group ORDER BY RANDOM())’. Then create temporary tables for each split using ‘CREATE TABLE AS SELECT FROM WHERE split_group = QUALIFY ROW NUMBER() OVER (ORDER BY RANDOM()) (SELECT COUNT( ) FROM transactions WHERE split_group — …) (respective split percentage);’

質問 62
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?

Increase the number of estimators (trees) in the Random Forest to the maximum possible value to capture all potential patterns, without cross validation.

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.

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.

Randomly select a small subset of the features (e.g., only use ‘SQUARE FOOTAGE and ‘NUM BEDROOMS) to simplify the model and prevent overfitting.

Eliminate outliers without understanding the data properly to reduce noise.

質問 63
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?

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.

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.

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.

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.

Immediately shut down the model to prevent further inaccurate classifications. Investigate why the recall is low before any retraining is performed.

質問 64
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)

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.

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.

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.

Coefficient analysis: By inspecting the coefficients of a linear model, we can easily determine feature importances.

Decision Tree visualization: Convert the model to decision trees and visualize it.

質問 65
You are tasked with preparing a Snowflake table named ‘PRODUCT REVIEWS’ for sentiment analysis. This table contains columns like ‘REVIEW ID, ‘PRODUCT ID’, ‘REVIEW TEXT’, ‘RATING’, and ‘TIMESTAMP’. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the ‘PRODUCT REVIEWS’ table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.

Using ‘ALTER TABLE DROP COLUMN’ to directly remove ‘TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: ‘ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;’

Creating a VIEW that only selects the ‘REVIEW _ TEXT , ‘REVIEW_ID’, and ‘RATING’ columns, effectively hiding the irrelevant columns from the model. SQL: ‘CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;’

creating a new table ‘REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , ‘REVIEW_ID’ , and ‘RATING’) using ‘CREATE TABLE AS SELECT. SQL: ‘CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;’

Dropping rows with ‘NULL’ values in REVIEW_TEXT and then dropping the ‘PRODUCT_ID’ and ‘TIMESTAMP’ columns using ‘ALTER TABLE. SQL: ‘CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;’

All of the above.

質問 66
Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

Using Snowflake’s built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake’s Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.

Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.

Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.

Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.

Utilizing Snowflake’s INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake’s data masking policies to control access to sensitive metadata.

質問 67
You are tasked with identifying fraudulent transactions in a large financial dataset stored in Snowflake using unsupervised learning. The dataset contains features like transaction amount, merchant ID, location, time, and user ID. You decide to use a combination of clustering and anomaly detection techniques. Which of the following steps and techniques would be MOST effective in achieving this goal while leveraging Snowflake’s capabilities and minimizing false positives?

Perform K-means clustering on the entire dataset using all available features, then flag any transaction that falls outside of any cluster as fraudulent. Ignore any feature selection or engineering to simplify the process.

Apply Principal Component Analysis (PCA) for dimensionality reduction, then use DBSCAN clustering to identify dense regions of normal transactions and flag any transaction that is not within a dense region as potentially fraudulent. After, review the anomalous data points.

Implement an Isolation Forest algorithm directly in SQL using complex JOINs and window functions to identify anomalies based on transaction volume and velocity.

Use a Snowflake Python UDF to perform feature selection, apply a combination of K-means clustering and anomaly detection techniques like Isolation Forest or Local Outlier Factor (LOF), and then score each transaction based on its likelihood of being fraudulent. Tune parameters and use a hold-out validation set to minimize false positives, using a Snowpark DataFrame to retrieve the data.

Use only the ‘transaction amount’ feature and perform histogram-based anomaly detection in Snowflake SQL by identifying values outside of the common ranges, disregarding other potentially relevant information.

質問 68
You are building a machine learning model to predict loan defaults. You have a dataset in Snowflake with the following features: ‘income’ (annual income in USD), ‘loan_amount’ (loan amount in USD), and ‘credit_score’ (FICO score). You need to normalize these features before training your model. The data has outliers in both ‘income’ and ‘loan_amount’, and ‘credit_score’ has a roughly normal distribution but you still want to standardize it to have a mean of 0 and standard deviation of 1. You want to perform these normalizations using only SQL in Snowflake (no UDFs). Which of the following SQL transformations are most suitable?

Option A

Option B

Option C

Option D

Option E

質問 69
You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The ‘CUSTOMER_TRANSACTIONS table contains a ‘TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as ‘High Value’ if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and ‘Low Value’ otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a ‘CUSTOMER SEGMENT column?

Option A

Option B

Option C

Option D

Option E

質問 70
You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot’s ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns ‘feature_name’, Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?

Option A

Option B

Option C

Option D

Option E

質問 71
You have trained a complex machine learning model using Snowpark for Python and are now preparing it for production deployment using Snowpark Container Services. You have containerized the model and pushed it to a Snowflake-managed registry. However, you need to ensure that only authorized users can access and deploy this model. Which of the following actions MUST you take to secure your model in the Snowflake Model Registry, ensuring appropriate access control, and minimizing the risk of unauthorized deployment or modification?

Grant the ‘USAGE privilege on the stage where the model files are stored to all users who need to deploy the model.

Grant the ‘READ privilege on the container registry to all users who need to deploy the model. Create a custom role with the ‘APPLY MASKING POLICY privilege and grant this role to the deployment team.

Grant the ‘USAGE privilege on the database and schema containing the model registry, grant the ‘READ privilege on the registry itself, and grant the EXECUTE TASK’ privilege to the deployment team for the deployment task.

Create a custom role, grant the USAGE’ privilege on the database and schema containing the model registry, grant the ‘READ privilege on the registry, and then grant this custom role to only those users authorized to deploy the model. Consider masking sensitive model parameters using masking policies.

Store the model outside of Snowflake managed registry and use external authentication to control access.

[2025年08月最新リリース]DSA-C03試験問題はあなたをパスさせる [Q54-Q71]

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[2025年08月最新リリース]DSA-C03試験問題はあなたをパスさせる [Q54-Q71]

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