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DSA-C03 - Trustable SnowPro Advanced: Data Scientist Certification Exam Exam Braindumps

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q164-Q169):

NEW QUESTION # 164
You are developing a real-time fraud detection system using Snowpark and deploying it as a Streamlit application connected to Snowflake. The system ingests transaction data continuously and applies a pre-trained machine learning model (stored as a binary file in Snowflake's internal stage) to score each transaction for fraud. You need to ensure the model loading process is efficient, and you're aiming to optimize performance by only loading the model once when the application starts, not for every single transaction. Which combination of approaches will BEST achieve this in a reliable and efficient manner, considering the Streamlit application's lifecycle and potential concurrency issues?

A. Use the 'st.cache_data' decorator in Streamlit to cache the loaded model and Snowpark session. Load the model directly from the stage within the cached function. This approach handles concurrency and ensures the model is only loaded once per session.
B. Use Python's built-in 'threading.Lock' to serialize access to the model loading code and the Snowpark session, preventing concurrent access from multiple Streamlit user sessions. Store the loaded model in a module-level variable.
C. Leverage the 'snowflake.snowpark.Session.read_file' to load the model binary directly into a Snowpark DataFrame and then convert to a Pandas DataFrame. Then, use the 'pickle' library for deserialization.
D. Load the model outside of the Streamlit application's execution context (e.g., in a separate script) and store it in a global variable. Access this global variable within the Streamlit application. This approach requires careful handling of concurrency.
E. Load the model within a try-except block and set the Snowpark session as a singleton that will guarantee model loads once for the entire application.

Answer: A

Explanation:
Option A is the best approach. 'st.cache_data' is the recommended way to cache data in Streamlit, including large objects like machine learning models. It automatically handles concurrency and ensures the model is only loaded once per Streamlit application instance. Because it's Streamlit's mechanism, it plays well with the Streamlit lifecycle. It is not required to use a Pandas DataFrame like option C suggests. Python global variables (B) are not suitable for web apps due to concurrency issues. While threading locks (D) could work, they add complexity and are generally less desirable than using Streamlit's caching mechanism. The model loading can be cached without a try-except block to set the Snowflake session as a singleton (E).

NEW QUESTION # 165
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. Load the scikit-learn model inside the UDF function on every invocation to ensure the latest version is used.
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. 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.
E. 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.

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 # 166
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?

A. Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
B. Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.
C. Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
D. Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.
E. Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.

Answer: A,B

Explanation:
Options C and E are the most effective for identifying outliers, considering the skewed nature of transaction data and location-specific variations. The IQR is better than mean and Standard Deviation. The MAD is more robust to outliers compared to standard deviation, which may be inflated by extreme values. Partitioning by location allows for a more nuanced identification of outliers specific to each location. DBSCAN is a great option to include with the partitioning because it considers transaction amount, location, and time as a factor in determine whether the data is an outlier. A and B are less effective because the median and standard deviation are sensitive to extreme values, and the IQR will not consider other dimensions such as location and time. D is only okay because it does not consider the impact of location on determining outliers.

NEW QUESTION # 167
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 B
B. Option A
C. Option D
D. Option C
E. Option E

Answer: A

Explanation:
Option B is the most efficient solution because it performs both the log transformation and the average calculation entirely within Snowflake using Snowpark functions. This avoids the overhead of transferring the data to the client side. It uses F.logl p() to apply the log transformation to the 'transaction_amount' column, handling potential zero values gracefully. It groups by 'customer_id' and uses F.mean() to calculate the average of the transformed transaction amounts.

NEW QUESTION # 168
You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A. Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
B. Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
C. Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
D. Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
E. Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.

Answer: E

Explanation:
Option D, leveraging Snowflake's native support for Streamlit, is the most appropriate and efficient approach. Streamlit allows you to build interactive web applications directly within the notebook, querying data directly from Snowflake and using Streamlit's built-in plotting capabilities (or integrating with other Python visualization libraries). This avoids pulling large amounts of data into the notebook's environment, which is crucial for large datasets. Option A is inefficient due to the data transfer overhead and limited interactivity. Option B can work but is not as streamlined as using Streamlit within the Snowflake environment. Option C will create static plots only. Option E is overly complex and less efficient than using Streamlit.

NEW QUESTION # 169
......

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