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Snowflake DSA-C03 試験シラバストピック:

Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題：

1. You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

A)

B)

C)

D)

E)

2. You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

A) The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
B) Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
C) Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
D) Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
E) The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.

3. You are working with a large sales transaction dataset in Snowflake, stored in a table named 'SALES DATA'. This table contains columns such as 'TRANSACTION_ID (unique identifier), 'CUSTOMER_ID', 'PRODUCT_ID, 'TRANSACTION_DATE' , and 'AMOUNT'. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the 'window.partitionBy()' and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column 'ROW NUM' to indicate the row number within each partition?

A)

B)

C)

D)

E)

4. 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.

A) 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.
B) 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.
C) 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.
D) 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.
E) 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.

5. You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

A) Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
B) Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
C) Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
D) Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
E) Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.

質問と回答：