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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

A) Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
B) Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
C) Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
D) Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.
E) Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom

2. You are developing a Spark application that needs to read data from a Snowflake table and write the processed data back to a different Snowflake table. Which of the following configurations and code snippets, used in conjunction with the Spark Snowflake Connector, would ensure secure and efficient data transfer, taking into account potential network latency and authentication best practices? Select all that apply.

A) Use Snowflake's Key Pair Authentication. Store the private key securely, and configure 'sflJRL', 'sflJser', 'private_key', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Ensure the user has appropriate Snowflake privileges. Configure 'numPartitions' parameter based on the scale of data to parallelize read and write operations.
B) Employ Snowflake's OAuth authentication. Obtain an OAuth token and pass it as a parameter to the Spark Snowflake Connector. You need to also provide 'sfDatabase' and 'sfSchemas properties. Don't set the number of partitions, leaving it to Spark's default behavior.
C)

D) Configure network timeout parameters in the Spark Snowflake Connector options to handle potential network latency, specifically 'networkTimeoutlnMilliSeconds'. Use 'PREACTIONS' and 'POSTACTIONS' to prepare and finalize data loading. Implement robust error handling to retry failed operations.
E) Set 'sfURL', 'sfUser', 'sfPassword', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Use to read data and to write data. Rely on Snowflake's default JDBC driver settings for network optimization.

3. You are performing exploratory data analysis on a dataset of customer transactions in Snowflake to prepare for a linear regression model that predicts transaction value based on several customer-related features (e.g., age, location, number of previous transactions). You suspect a non-linear relationship between 'customer_age' and 'transaction_value'. Which of the following Snowflake SQL techniques is MOST appropriate for exploring and potentially transforming the 'customer_age' variable to better fit a linear regression model?

A) Use the window function to bin 'customer_age' into quartiles and treat each quartile as a categorical variable in the linear regression model.
B) Create polynomial features by adding 'customer_ageA2' and 'customer_ageA3' as new columns to the table, without checking for interaction effects.
C) Implement a Box-Cox transformation in Snowpark Python, select a suitable transformation parameter based on the data, and apply the transformation on 'customer_age' feature.
D) Calculate the Pearson correlation coefficient between 'customer_age' and 'transaction_value' using the function. If the correlation is low, discard the 'customer_age' variable.
E) Apply a logarithmic transformation to 'customer_age' if a scatter plot of 'customer_age' vs 'transaction_value' shows a curve that flattens out as 'customer_age' increases.

4. You are working with a Snowflake table named 'CUSTOMER DATA' that contains personally identifiable information (PII), including customer names, email addresses, and phone numbers. Your team needs to perform exploratory data analysis on this data to understand customer demographics and behavior. However, you must ensure that the PII is protected and that only authorized personnel can access the sensitive information. Which of the following strategies should you implement in Snowflake to achieve secure EDA?

A) Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.
B) Create a view on top of that excludes the PII columns (e.g., name, email, phone). Grant 'SELECT privileges on this view to data scientists. Also implement data masking policies on the 'CUSTOMER DATA' table for the PII columns and grant 'SELECT on the table to specific roles requiring access to the masked values.
C) Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.
D) Apply dynamic data masking to the entire 'CUSTOMER_DATA' table, masking all columns by default, and provide decryption keys only to authorized users.
E) Create a copy of the 'CUSTOMER DATA table without the PII columns and grant 'SELECT' privileges on this copy to the data scientists. Use masking policies on the original table.

5. You are tasked with automating the retraining of a Snowpark ML model based on the performance metrics of the deployed model. You have a table 'MODEL PERFORMANCE that stores daily metrics like accuracy, precision, and recall. You want to automatically trigger retraining when the accuracy drops below a certain threshold (e.g., 0.8). Which of the following approaches using Snowflake features and Snowpark ML is the MOST robust and cost-effective way to implement this automated retraining pipeline?

A) Implement a Snowpark ML model training script that automatically retrains the model every day, regardless of the performance metrics. This script will overwrite the previous model.
B) Create a Dynamic Table that depends on the 'MODEL PERFORMANCE table and materializes when the accuracy is below the threshold. This Dynamic Table refresh triggers a Snowpark ML model training stored procedure. This stored procedure saves the new model with a timestamp and updates a metadata table with the model's details.
C) Create a Snowflake task that runs every hour, queries the 'MODEL_PERFORMANCE table, and triggers a Snowpark ML model training script if the accuracy threshold is breached. The training script will overwrite the existing model.
D) Implement an external service (e.g., AWS Lambda or Azure Function) that periodically queries the "MODEL_PERFORMANCE table using the Snowflake Connector and triggers a Snowpark ML model training script via the Snowflake API.
E) Use a Snowflake stream on the 'MODEL_PERFORMANCE table to detect changes in accuracy, and trigger a Snowpark ML model training function using a PIPE whenever the accuracy drops below the threshold.

Solutions: