Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03

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Snowflake SnowPro Advanced: Data Scientist Certification DSA-C03 Prüfungsfragen mit Lösungen:

1. You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting model, you want to understand the relationship between 'number_of_products' and the churn probability. You generate a partial dependence plot (PDP) for 'number_of_products'. The PDP shows a steep increase in churn probability as 'number_of_products' increases from 1 to 3, followed by a plateau. Which of the following statements are the MOST accurate interpretations of this PDP? Assume the dataset is balanced and has undergone proper preprocessing.

A) There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.
B) Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.
C) The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.
D) Increasing the number of products purchased by all customers will definitively reduce overall churn.
E) The model is perfectly calibrated, and the PDP accurately represents the true causal effect of 'number_of_products' on churn.

2. You are building an automated model retraining pipeline for a sales forecasting model in Snowflake using Snowflake Tasks and Stored Procedures. After retraining, you want to validate the new model against a champion model already deployed. You need to define a validation strategy using the following models: champion model deployed as UDF "FORECAST UDF , and contender model deployed as UDF 'FORECAST UDF NEW'. Given the following objectives: (1) Minimal impact on production latency, (2) Ability to compare predictions on a large volume of real-time data, (3) A statistically sound comparison metric. Which of the following SQL statements best represents how to efficiently compare the forecasts of the two models on a sample dataset and calculate the Root Mean Squared Error (RMSE) to validate the new model?

A)

B)

C)

D)

E)

3. You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

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

4. You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A) Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
B) Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.
C) Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
D) Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
E) Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.

5. A data scientist is analyzing website click-through rates (CTR) for two different ad campaigns. Campaign A ran for two weeks and had 10,000 impressions with 500 clicks. Campaign B also ran for two weeks with 12,000 impressions and 660 clicks. The data scientist wants to determine if there's a statistically significant difference in CTR between the two campaigns. Assume the population standard deviation is unknown and unequal for the two campaigns. Which statistical test is most appropriate to use, and what Snowflake SQL code would be used to approximate the p-value for this test (assume 'clicks_b' , and are already defined Snowflake variables)?

A) An independent samples t-test, because we are comparing the means of two independent samples. Snowflake code: SELECT

B) An independent samples t-test (Welch's t-test), because we are comparing the means of two independent samples with unequal variances. Snowflake code (approximation using UDF - assuming UDF 'p_value_from_t_stat' exists that calculates p-value from t-statistic and degrees of freedom):

C) A one-sample t-test, because we are comparing the sample mean of campaign A to the sample mean of campaign Snowflake code: 'SELECT t_test_lsamp(clicks_a/impressions_a - clicks_b/impressions_b, 0)'
D) Az-test, because we know the population standard deviation. Snowflake code: 'SELECT normcdf(clicks_a/impressions_a - clicks_b/impressions_b, O, 1)'
E) A paired t-test, because we are comparing two related samples over time. Snowflake code: 'SELECT t_test_ind(clicks_a/impressions_a, 'VAR EQUAL-TRUE')

Fragen und Antworten: