Snowflake DSA-C03

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

1. A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?

A) Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.
B) Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.
C) Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.
D) Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
E) Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.

2. You are tasked with performing exploratory data analysis on a table named containing daily sales transactions. The table includes columns like 'transaction_date', 'product_id', 'quantity' , and 'price'. Your goal is to identify potential data quality issues and understand the distribution of sales. Which of the following SQL queries using Snowflake's statistical functions and features would be MOST effective for quickly identifying outliers in the 'quantity' column, potential data skewness, and missing values?

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

3. You are building a machine learning model using Snowflake data to predict customer churn. Your dataset includes a 'CUSTOMER TYPE column with the following possible values: 'New', 'Returning', and 'VIP'. You need to perform one-hot encoding on this column. Which of the following Snowflake SQL queries correctly implements one-hot encoding for the 'CUSTOMER TYPE column, creating separate binary columns for each customer type ('IS NEW', 'IS RETURNING', 'IS VIP')?

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

4. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

A) Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
B) Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
C) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
D) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
E) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.

5. 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) Ensure that the samples are drawn independently and randomly.
B) Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).
C) 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.
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.

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