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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
You've built a regression model in Snowflake to predict customer churn. You've calculated the R-squared score on your test data and found it to be 0.65. However, after deploying the model to production and monitoring its performance over several weeks, you notice the model's predictive accuracy has significantly decreased. Which of the following factors could contribute to this performance degradation?
Select all that apply.

A. Data drift: The distribution of the input features in the production data has changed significantly compared to the training data.
B. Bias Variance trade off : Model is having high bias.
C. Increased data volume: The production data volume has increased significantly, causing resource contention and impacting model performance in Snowflake.
D. Feature engineering inconsistencies: The feature engineering steps applied to the production data are different from those applied during training.
E. Overfitting: The model learned the training data too well, capturing noise and specific patterns that do not generalize to new data.

Answer: A,D,E

Explanation:
Options A, B, and C are all potential causes of performance degradation in a deployed regression model. Data drift (A) means the characteristics of the input data have changed, invalidating the model's assumptions. Overfitting (B) causes the model to perform poorly on unseen data. Feature engineering inconsistencies (C) introduce errors because the model expects features transformed in a specific way. Option D is less likely to be a direct cause of predictive degradation. Increased data volume might impact query performance or resource utilization but would not directly impact the model accuracy, if infrastructure has allocated adequetly. Option E would affect performance both during training and testing. Since R-squared is already low so model is already suffering from high bias

NEW QUESTION # 102
You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

Answer: E

Explanation:
Option B, using is the most efficient. The method directly retrieves the data as a Pandas DataFrame, leveraging Snowflake's internal optimizations for transferring data to Pandas. It's significantly faster than fetching rows individually or all at once and then creating the DataFrame. Also, it only selects the needed Columns. Option A fetches all columns and then tries to build dataframe from the list which is less effective. Option C would require additional setup with sqlalchemy and may introduce extra dependencies. Option D is also correct, but option B utilizes snowflake's internal optimizations for pandas retrieval making it best choice. Option E is also not effective as it only fetches 1000 records.

NEW QUESTION # 103
You are building a data science pipeline in Snowflake to predict customer churn. The pipeline includes a Python UDF that uses a pre- trained scikit-learn model stored as a binary file in a Snowflake stage. The UDF needs to load this model for prediction. You've encountered an issue where the UDF intermittently fails, seemingly related to resource limits when multiple concurrent queries invoke the UDF. Which of the following strategies would best optimize the UDF for concurrency and resource efficiency, minimizing the risk of failure?

A. Utilize Snowflake's session-level caching by storing the loaded model in 'session.get('model')' to be reused across multiple UDF calls within the same session. Reload the model if 'session.get('model')' is None.
B. Implement a global, lazy-loaded cache for the scikit-learn model within the UDF's module. The model is loaded only once during the first invocation and shared across subsequent calls. Protect the loading process with a lock to prevent race conditions in concurrent environments.
C. Increase the memory allocated to the Snowflake warehouse to accommodate multiple UDF invocations.
D. Load the scikit-learn model outside the UDF function in the global scope of the module so that all invocations share the same loaded model instance. Use the 'context.getExecutionContext(Y to track execution, making sure it is thread safe.
E. Load the scikit-learn model inside the UDF function on every invocation to ensure the latest version is used.

Answer: B

Explanation:
Option D provides the most efficient and robust solution. Loading the model only once (lazy loading) reduces overhead. A global cache ensures reusability. A lock is crucial to prevent race conditions during the initial loading in a concurrent environment. Option A is inefficient due to repeated loading. Option B is problematic because Snowflake UDFs do not directly support global variables in a thread-safe manner. Option C is incorrect as 'session.get' is not a valid Snowflake API for Python UDFs and lacks thread safety. Option E, while potentially helpful, doesn't address the underlying inefficiency of repeatedly loading the model.

NEW QUESTION # 104
A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?

A. Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.
B. Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
C. While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
D. Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).
E. Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.

Answer: A,B,D,E

Explanation:
Option A is true because label encoding converts strings into integers, which are more memory-efficient than storing numerous one-hot encoded columns. Snowflake's columnar storage further optimizes integer storage. Option B is also true; label encoding inherently creates an ordinal relationship that might not be valid for nominal features like city names. Option C is incorrect; simple binarization (e.g., > threshold) of label encoded data doesn't remove the arbitrary ordinal relationship; more complex binarization techniques would be needed. Option D is accurate; binarization reduces dimensionality but sacrifices granularity, leading to information loss. Option E is correct because carefully chosen thresholds might correlate with the target variable and improve predictive power.

NEW QUESTION # 105
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 single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
B. Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness 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. 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.
E. 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.

Answer: D,E

Explanation:
Option B is correct because breaking down the transformations into smaller Dynamic Tables with tailored refresh intervals ensures that only necessary data is recomputed, minimizing cost and resource usage. Option D is also correct because combining Dynamic Tables for feature engineering with Snowpark ML allows for efficient model training and deployment within Snowflake, leveraging the platform's scalability and security features. Furthermore, specifying refresh intervals based on data freshness guarantees that the model is trained on up-to-date information. Option A could lead to unnecessary computations if the entire table needs to be refreshed even for minor changes. Option C relies on traditional tables and stored procedures, which may be less efficient and harder to manage than Dynamic Tables. Option E ignores data freshness, which can significantly impact model accuracy.

NEW QUESTION # 106
......

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