Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03認證

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Snowflake DSA-C03 考試大綱主題:

最新的 SnowPro Advanced DSA-C03 免費考試真題：

1. A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?

A) Verify that the sample size (n=50) is sufficiently large to approximate normality of the sample mean distribution based on the CLT. This implicitly assumes population size is significantly larger than the sample size.
B) Ensure that the samples are drawn independently and randomly.
C) Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).
D) Calculate the standard deviation of the sample means and use it as an estimate for the standard error of the mean.
E) Check if the original population distribution (purchase amounts) is approximately normally distributed.

2. 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) 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.
B) 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.
C) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
D) Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
E) 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.

3. You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

A)

B)

C)

D)

E)

4. You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?

A) Query: 'SELECT session_duration FROM user_behavior WHERE user_segments = 'New';- (repeated for each user segment). Visualization: Overlayed histograms showing the distribution of session duration for each user segment on the same axes.
B) Query: 'SELECT user_segments, APPROX 0.25), APPROX 0.5), APPROX_PERCENTlLE(session_duration, 0.75) FROM user_behavior GROUP BY user_segments;' Visualization: Scatter plot where each point represents a user segment and the x,y coordinates represent session duration at 25th and 75th percentiles respectively.
C) Query: 'SELECT user_segments, MEDIAN(session_duration) FROM user_behavior GROUP BY user_segments;' Visualization: Box plot showing the distribution (quartiles, median, outliers) of session duration for each user segment.
D) Query: ' SELECT COUNT( ) ,user_segments FROM user_behavior GROUP BY user_segments;' Visualization: Pie chart showing proportion of each segment.
E) Query: 'SELECT user_segments, AVG(session_duration) FROM user_behavior GROUP BY Visualization: Bar chart showing average session duration for each user segment.

5. You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

A) Increase the regularization strength in your model.
B) Remove outlier data points to improve overall model performance.
C) Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
D) Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
E) Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).

問題與答案：