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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) 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.
B) Apply dynamic data masking to the entire 'CUSTOMER_DATA' table, masking all columns by default, and provide decryption keys only to authorized users.
C) Use transient tables to store the customer data after PII is obfuscated, drop the table and reload new data daily.
D) 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.
E) Grant 'SELECT privileges on the 'CUSTOMER DATA' table to all data scientists, and rely on them to avoid querying PII columns directly.

2. You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named 'transactions'. You need to create a new feature called 'transaction_size category' based on the 'transaction_amount' column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake's parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?

A)

B)

C)

D)

E)

3. A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

A) Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.
B) Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
C) Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.
D) Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.
E) Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.

4. You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A) Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling.
B) Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics.
C) Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.
D) The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
E) Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small.

5. You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
B) Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
C) Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
D) Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
E) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.

Solutions: