Snowflake DSA-C03 問題集

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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 developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?

A) Store the private key in a password-protected ZIP archive and extract it during the Snowflake connection process.
B) Use the Snowflake CLI to generate a temporary access token and hardcode it into your VS Code script for authentication.
C) Store the private key in a secure database table within Snowflake and query it dynamically.
D) Store the encrypted private key in a configuration file within your VS Code project and decrypt it at runtime using a password-based encryption algorithm.
E) Store the private key in a secure vault (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and retrieve it dynamically within your VS Code script using the appropriate API or SDK.

2. You are working with a Snowflake table 'CUSTOMER TRANSACTIONS containing customer IDs, transaction dates, and transaction amounts. You need to identify customers who are likely to churn (stop making transactions) in the next month using a supervised learning model. Which of the following strategies would be MOST appropriate to define the target variable (churned vs. not churned) and create features for this churn prediction problem, suitable for a Snowflake-based machine learning pipeline?

A) Define churn as customers with zero transactions in the last month. Create features like average transaction amount over the past year, number of transactions in the past month, and recency (time since the last transaction).
B) Define churn as customers who haven't made a transaction in the past 6 months. Create a single feature representing the total number of transactions the customer has ever made.
C) Define churn as customers with no transactions in the next month (the prediction target). Create features including: Recency (days since last transaction), Frequency (number of transactions in the past 3 months), Monetary Value (average transaction amount over the past 3 months), and trend of transaction amounts (using linear regression slope over the past 6 months).
D) Define churn as customers with a significant decrease (e.g., 50%) in transaction amounts compared to the previous month. Create features based on demographic data and customer segmentation information, joined from other Snowflake tables.
E) Define churn based on a fixed threshold of total transaction value over a predefined period. Feature Engineering should purely consist of time series decomposition using Snowflake's built-in functions.

3. You are using the NetworkX library in Snowpark Python to analyze social network data stored in a Snowflake table named 'USER CONNECTIONS', which has columns 'USER ID' and 'CONNECTED USER representing connections between users. You want to find the users with the highest 'betweenness centrality' to identify influential nodes in the network. Which Snowpark Python code snippet would correctly calculate and display the top 5 users with the highest betweenness centrality?

A)

B)

C)

D)

E)

4. You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?

A) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
B) Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
C) Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.
D) Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
E) Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.

5. A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

A)

B)

C)

D)

E)

質問と回答：