Snowflake SnowPro Advanced DSA-C03

可以保證你100%通過Snowflake認證DSA-C03考試

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付款成功後, 我們的體統將自動通過電子郵箱將你已購買的DSA-C03題库(SnowPro Advanced: Data Scientist Certification Exam)發送到你的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查你的垃圾郵件。）

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消費保證

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最新的 SnowPro Advanced DSA-C03 免費考試真題:

1. You have trained a fraud detection model using scikit-learn and want to deploy it in Snowflake using the Snowflake Model Registry. You've registered the model as 'fraud _ model' in the registry. You need to create a Snowflake user-defined function (UDF) that loads and executes the model. Which of the following code snippets correctly creates the UDF, assuming the model is a serialized pickle file stored in a stage named 'model_stage'?

A) Option E
B) Option C
C) Option B
D) Option D
E) Option A

2. 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?

A)

B)

C)

D)

E)

3. You are a data scientist working for a retail company. You've been tasked with identifying fraudulent transactions. You have a Snowflake table named 'TRANSACTIONS' with columns 'TRANSACTION ID', 'AMOUNT', 'TRANSACTION DATE', 'CUSTOMER ID', and 'LOCATION'. You suspect outliers in transaction amounts might indicate fraud. Which of the following SQL queries is the MOST efficient and appropriate to identify potential outliers using the Interquartile Range (IQR) method, and incorporate necessary data type considerations for robust percentile calculations? Consider also the computational cost associated with each approach on a large dataset.

A) Option E
B) Option C
C) Option B
D) Option D
E) Option A

4. You are tasked with deploying a real-time fraud detection model in Snowflake. The model requires very low latency (under 100ms) to prevent fraudulent transactions. The input data is streamed into a Snowflake table. You are considering using either a Scalar or Vectorized Python UDF for scoring. Which of the following approaches and considerations are MOST critical for achieving the desired performance and reliability? Assume the model itself is computationally inexpensive. Select all that apply.

A) Pre-load the model into a static variable within the UDF code, ensuring it's only loaded once per worker node.
B) Use a Vectorized UDF with a small 'MAX BATCH_SIZE to minimize latency while still leveraging vectorization benefits.
C) Configure Snowflake's Auto-Suspend feature to aggressively suspend the warehouse when idle, to minimize costs.
D) Use a Scalar UDF because it has lower overhead per invocation compared to a Vectorized UDF when processing individual transactions.
E) Utilize Snowflake's Materialized Views to pre-compute frequently used features, reducing the amount of data the UDF needs to process.

5. A marketing team uses Snowflake to store customer purchase data'. They want to segment customers based on their spending habits using a derived feature called The 'PURCHASES' table has columns 'customer id' (IN T), 'purchase_date' (DATE), and 'purchase_amount' (NUMBER). The team needs a way to handle situations where a customer might have missing months (no purchases in a particular month). They want to impute a 0 spend for those months before calculating the average. Which approach provides the most accurate and robust calculation, especially when considering users with sparse purchase history?

A) Calculate the average monthly spend directly from the 'PURCHASES' table without accounting for missing months: 'AVG(purchase_amount) GROUP BY customer_id, date_trunc('month',
B) Calculate the total spend for each customer and divide by the number of months since their first purchase: / DATEDlFF(month, CURRENT DATE()) GROUP BY customer_id'.
C) Create a view containing all months for each customer, left join with the 'PURCHASES' table, impute 0 for null 'purchase_amounts values, and then calculate the average spend. Requires creating a helper table for all the month.
D) Calculate the average spend only for customers with purchases in every month of the year. Ignore other customers in the analysis.
E) Use a window function to calculate the average spend over a fixed window of the last 3 months, ignoring missing months in the calculation.

問題與答案：