DSA-C03試験感想 & DSA-C03最新対策問題

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Snowflake SnowPro Advanced: Data Scientist Certification Exam 認定 DSA-C03 試験問題 (Q69-Q74):

質問 # 69
You are evaluating a binary classification model's performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables 'predictions' with columns 'predicted_probability' (FLOAT) and 'actual_value' (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the 'True positive Rate' and 'False positive Rate' for different thresholds

A. Calculate AUC directly within a Snowpark Python UDF using scikit-learn's function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.
B. Export the 'predicted_probability' and 'actual_value' columns to a local Python environment and calculate the AUC using scikit-learn.
C. The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.
D. Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.
E. The best way to calculate AUC is to randomly guess the probabilities and see how it performs.

正解：A、D

解説：
Options A and C are correct. Option A demonstrates calculating AUC directly within Snowflake using a Snowpark Python UDF and scikit-learn's . This is efficient for large datasets as it avoids data transfer. Option C correctly outlines the process of calculating TPR and FPR using SQL and approximating AUC using the trapezoidal rule, another viable approach within Snowflake. Option B is incorrect; AUC can be calculated reliably within Snowflake. Option D is inefficient due to data transfer. Option E is blatantly incorrect.

質問 # 70
You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A. Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
B. The production environment has significantly more missing data compared to the training and validation environments.
C. Your training and validation sets are not representative of the real-world production data due to sampling bias.
D. There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
E. The AUC metric is inherently unreliable and should not be used for model evaluation.

正解：A、B、C、D

解説：
A, B, C, and D are all valid reasons for performance degradation in production. Sampling bias (A) means the training/validation data doesn't accurately reflect the production data. Temporal bias (B) arises when customer behavior changes over time. Overfitting (C) leads to good performance on the training/validation set but poor generalization to new data. Missing data (D) can negatively impact the model's ability to make accurate predictions. AUC is a reliable metric, especially when combined with other metrics, so E is incorrect.

質問 # 71
You're building a customer segmentation model and need to aggregate data from various tables. You have the following tables in Snowflake: 'customer demographics' (customer id, age, city, income) 'customer transactionS (transaction_id, customer id, transaction_date, amount) 'product_details' (product_id, category) 'transaction_products' (transaction_id, product_id) Your goal is to create a single Snowpark DataFrame containing customer demographics along with the total amount spent by each customer on products within the 'Electronics' category in the last year. However, ensure that only customers with income greater than 50000 are considered and handle cases where customers have no transaction records, assigning a value of 0 to the 'total_electronics_spending' column for those customers. How can we achieve this using snowpark? Choose the correct options

A. Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.
B. Create a temporary view to store total electronics expenditure of each customer and left join with customer demographics table.
C. Use a series of INNER JOINs to connect the tables and filter data, followed by grouping and aggregation. This approach guarantees accurate results with good performance.
D. Use a combination of LEFT JOINs and filtering. Start with 'customer_demographics' (filtered for income > 50000) as the base table and LEFT JOIN to subsequent tables. Use the 'coalesce' function to handle customers without transaction data.
E. Create a complex SQL query within Snowpark using 'session.sql()' to perform all the joins, filtering, and aggregation in a single step. This will be the most efficient approach.

正解：B、D、E

解説：
Option B, C and D are correct. Option B is correct because using LEFT JOINs starting with 'customer_demographics' (after filtering for income) ensures all eligible customers are included. 'coalesce' is crucial for handling customers with no transactions, assigning a 0 value. Option C is also correct as using a temporary view is a valid solution to have electronics expenditure for each customer. Option D is correct as pushing down all operations to SQL within Snowpark can be highly performant, as it allows Snowflake to optimize the query execution. However, query readability and maintainability should also be considered. Option A is incorrect because it states that INNER JOINs should be used, but inner joins would exclude customers with no transaction data which is opposite to what is stated in the question. Option E is incorrect as UDFs can introduce performance overhead compared to native Snowpark DataFrame operations or direct SQL queries, especially for large datasets. Avoid UDF when the same output can be achieved without it.

質問 # 72
You have trained a machine learning model in Snowflake using Snowpark Python to predict customer churn. You want to deploy this model as a Snowflake User-Defined Function (UDF) for real-time scoring of new customer data arriving in a stream. The model uses several external Python libraries not available by default in the Anaconda channel. Which sequence of steps is the MOST efficient and correct way to deploy the model within Snowflake to ensure all dependencies are met?

A. Create a Snowflake stage, upload the model file and a 'requirements.txt' file listing the dependencies. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the 'imports' parameter with the model file and requirements.txt. Snowflake will automatically install the dependencies from the 'requirements.txt' file during UDF execution.
B. Create a Snowflake stage and upload the model file. Create a conda environment file ('environment.yml') specifying the dependencies. Upload the environment.yml file to the stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and the environment.yml file in the 'imports' and 'packages' parameters, respectively. Snowflake will create a conda environment based on the environment.yml file during UDF execution.
C. Create a Snowflake stage, upload the model file and all dependency .py' files. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the 'imports parameter with all the file names. Snowflake will interpret all .py' files as module for UDF execution.
D. Create a virtual environment locally with all required dependencies installed. Package the entire virtual environment into a zip file. Upload the zip file to a Snowflake stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the zip file in the 'imports' parameter. Snowflake will automatically extract the zip and use the virtual environment during UDF execution.
E. Package the model file and all dependencies into a single Python wheel file. Upload this wheel file to a Snowflake stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the wheel file in the 'imports' parameter. Snowflake will automatically install the wheel during UDF execution.

正解：E

解説：
Packaging the model and its dependencies into a single Python wheel file is the recommended and most efficient approach. Uploading the wheel to a stage and referencing it in the 'imports' parameter allows Snowflake to handle dependency resolution seamlessly. Options A and C assume Snowflake can directly install dependencies from a requirements.txt or environment.yml file, which is not directly supported. Option D is unnecessarily complex as it involves packaging an entire virtual environment. Option E will not handle complex external packages.

質問 # 73
You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

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

正解：A、B、C、E

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

質問 # 74
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