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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

A) Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
B) Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.
C) Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.
D) Increase the learning rate significantly to speed up convergence during training.
E) Reduce the number of features by randomly removing some columns, as this always prevents overfitting.

2. You are building an image classification model within Snowflake to categorize satellite imagery based on land use types (residential, commercial, industrial, agricultural). The images are stored as binary data in a Snowflake table 'SATELLITE IMAGES. You plan to use a pre-trained convolutional neural network (CNN) from a library like TensorFlow via Snowpark Python UDFs. The model requires images to be resized and normalized before prediction. You have a Python UDF named that takes the image data and model as input and returns the predicted class. What steps are crucial to ensure optimal performance and scalability of the image classification process within Snowflake, considering the volume and velocity of incoming satellite imagery?

A) Implement image resizing and normalization directly within the 'classify_image' Python UDF using libraries like OpenCV. Ensure the UDF is vectorized to process images in batches and leverage Snowpark's optimized data transfer capabilities.
B) Utilize Snowflake's external functions to call an image processing service hosted on AWS Lambda or Azure Functions for image resizing and normalization, then pass the processed images to the 'classify_image' UDF.
C) Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
D) Use a combination of Snowpark Python UDFs for preprocessing tasks like resizing and normalization, and leverage Snowflake's GPU-accelerated warehouses (if available) to expedite the inference step within the 'classify_image' UDF. Ensure the model weights are efficiently cached.
E) Pre-process the images outside of Snowflake using a separate data pipeline and store the resized and normalized images in a new Snowflake table before running the 'classify_image' UDE

3. 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) 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.
B) 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.
C) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
D) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
E) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.

4. Which of the following statements are TRUE regarding the 'Data Understanding' and 'Data Preparation' steps within the Machine Learning lifecycle, specifically concerning handling data directly within Snowflake for a large, complex dataset?

A) Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
B) Data Understanding primarily involves identifying potential data quality issues like missing values, outliers, and inconsistencies, and Snowflake features like 'QUALIFY and 'APPROX TOP can aid in this process.
C) During Data Preparation, you should always prioritize creating a single, wide table containing all possible features to simplify the modeling process.
D) Data Preparation in Snowflake can involve feature engineering using SQL functions, creating aggregated features with window functions, and handling missing values using 'NVL' or 'COALESCE. Furthermore, Snowpark Python provides richer data manipulation using DataFrame APIs directly on Snowflake data.
E) The 'Data Understanding' step is unnecessary when working with data stored in Snowflake because Snowflake automatically validates and cleans the data during ingestion.

5. You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A) Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
B) Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.
C) Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
D) Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.
E) Encrypt the entire database containing the transaction data to protect PII from unauthorized access.

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