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

1. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

2. You are tasked with training a model within Snowflake to predict customer churn for a telecommunications company. The dataset is stored in a Snowflake table named 'CUSTOMER DATA. The features include 'age', and 'data_usage'. The target variable is 'churned' (boolean). You want to use the SNOWFLAKE.ML.ANACONDA INTEGRATION to leverage Scikit-learn for model training. Which of the following code snippets correctly performs model training with Snowflake ML, addressing potential issues like feature scaling and data type handling within the stored procedure?

A)

B)

C)

D)

3. You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A) Accuracy and Recall.
B) Precision and Fl-score.
C) Accuracy and ROC AUC.
D) Precision, Recall, and Fl-score.
E) ROC AUC and Recall.

4. You are tasked with analyzing the 'transaction amounts' column in the 'sales data' table to understand its variability across different geographical regions. You need to calculate the variance of transaction amounts for each region. However, some regions have very few transactions, which can skew the variance calculation. Which of the following SQL statements correctly calculates the variance for each region, excluding regions with fewer than 10 transactions, using Snowflake's native statistical functions?

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

5. You are working with a Snowflake table 'CUSTOMER DATA containing customer information for a marketing campaign. The table includes columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS, 'CITY, 'STATE, ZIP CODE, 'COUNTRY, 'PURCHASE HISTORY, 'CLICKSTREAM DATA, and 'OBSOLETE COLUMN'. You need to prepare this data for a machine learning model focused on predicting customer churn. Which of the following strategies and Snowpark Python code snippets would be MOST efficient and appropriate for removing irrelevant fields and handling potentially sensitive personal information while adhering to data governance policies? Assume data governance requires removing personally identifiable information (PII) that isn't strictly necessary for the churn model.

A) Dropping columns 'OBSOLETE_COLUMN' directly. Then, for PII columns ('FIRST_NAME, 'LAST_NAME, 'EMAIL', 'PHONE_NUMBER, 'ADDRESS', 'CITY', 'STATE' , , 'COUNTRY), create a separate table with anonymized or aggregated data for analysis unrelated to the churn model. Use Keep all PII columns but encrypt them using Snowflake's built-in encryption features to comply with data governance before building the model. Drop 'OBSOLETE COLUMN'.
B) Drop 'OBSOLETE_COLUMN'. For columns like and 'LAST_NAME' , consider aggregating into a single 'FULL_NAME feature if needed for some downstream task. Apply hashing or tokenization techniques to sensitive PII columns like and 'PHONE NUMBER using Snowpark UDFs, depending on the model's requirements. Drop columns like 'ADDRESS, 'CITY, 'STATE, ZIP_CODE, 'COUNTRY as they likely do not contribute to churn prediction. Example hashing function:

C) Keeping all columns as is and providing access to Data Scientists without any changes, relying on role based security access controls only.
D) Dropping 'FIRST NAME, UST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS', 'CITY, 'STATE', ZIP CODE, 'COUNTRY and 'OBSOLETE_COLUMN' columns directly using 'LAST_NAME', 'EMAIL', 'PHONE_NUMBER', 'ADDRESS', 'CITY', 'STATE', 'ZIP_CODE', 'COUNTRY', without any further consideration.