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

NEW QUESTION # 101
A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

Answer: C,E

Explanation:
Options A and E are correct. Option A uses the 'scipy.stats.norm.intervar function correctly to compute the confidence interval for a proportion. Option E manually calculates the confidence interval using the standard error and the z-score for a 95% confidence level (approximately 1.96). Option B uses the t-distribution which is unnecessary for large sample sizes and is inappropriate here given the context. Option C is not the correct way to calculate the confidence interval for proportion using binomial distribution interval function, it calculates range of values in dataset, instead of confidence interval. Option D uses incorrect standard deviation.

NEW QUESTION # 102
You are tasked with building a data pipeline using Snowpark Python to process customer feedback data stored in a Snowflake table called FEEDBACK DATA'. This table contains free-text feedback, and you need to clean and prepare this data for sentiment analysis. Specifically, you need to remove stop words, perform stemming, and handle missing values. Which of the following code snippets and strategies, potentially used in conjunction, provide the most effective and performant solution for this task within the Snowpark environment?

A. Leverage Snowflake's built-in string functions within SQL to remove common stop words based on a predefined list. Use a Snowpark DataFrame to execute this SQL transformation. For stemming, research and deploy a Java UDF implementing stemming algorithms, then chain it within a Snowpark transformation pipeline. Replace missing values with the string 'N/A' during the DataFrame construction using 'na.fill('N/A')'.
B. Utilize Snowpark's 'call_function' with a Java UDF pre-loaded into Snowflake, which removes stop words and performs stemming with libraries like Lucene. Missing values can be handled with SQL's 'NVL' function during the initial data extraction into a Snowpark DataFrame.
C. Use a Python UDF that utilizes the NLTK library to remove stop words and perform stemming on the feedback text. Handle missing values by replacing them with an empty string using the .fillna(")' method on the Snowpark DataFrame after applying the UDF.
D. Implement all data cleaning tasks within a single SQL stored procedure including removing stop words using REPLACE functions, stemming using a custom lookup table, and handling NULL values using COALESC Call this stored procedure from Snowpark for Python.
E. Load the FEEDBACK DATA' table into a Pandas DataFrame using perform stop word removal and stemming using libraries like spacy or NLTK, handle missing values using Pandas' 'fillna()' method. Then, convert the cleaned Pandas DataFrame back into a Snowpark DataFrame. Use vectorization of text column in dataframe after above step

Answer: A,B

Explanation:
Options B and C provide the most effective and performant solutions.Option B leverages a combination of SQL and Java UDF to efficiently handle different parts of the cleaning process. The use of Snowflake's built-in string functions for removing stop words in SQL is efficient for common stop words, and Java UDF provides a more flexible and potentially more efficient solution for stemming. DataFrame .na.fill' is the most appropriate way to fill the missing values during the DataFrame creation. Option C: Utilizes pre-loaded Java UDFs for word processing, combined with SQL's NVL for missing value handling, is a strategy to leverage different components of Snowflake for performance and efficiency.Option A: While Python UDFs are flexible, they can be less performant than SQL or Java UDFs, especially for large datasets. Loading entire dataframe is an anti pattern. Also using .fillna on the dataframe instead of on the dataframe construction will reduce the performance. Option D: Loading all data into pandas is a bad habit and might reduce the performance. Also vectorization is not appropriate for cleaning the data. Option E: Stored procedures can be performant, relying solely on nested REPLACE functions for stop word removal can be cumbersome, and difficult to maintain compared to other approaches.

NEW QUESTION # 103
You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A. Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
B. Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.
C. Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
D. Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
E. Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.

Answer: B

Explanation:
Option B is the MOST effective way to minimize memory consumption within the Python stored procedure. The 'hist' tree method in XGBoost uses a histogram-based approach for finding the best split points, which is more memory-efficient than the exact tree method. Gradient- based sampling ('goss') reduces the number of data points used for calculating the gradients, further reducing memory usage. Tuning 'max_depth' and helps to control the complexity of the trees, preventing them from growing too large and consuming excessive memory. Converting categorical features to numerical is crucial as categorical features when One Hot Encoded, can explode feature space and significantly increase memory footprint. Option A will not work directly within Snowflake as Dask is not supported on warehouse compute. Option C may reduce the accuracy of the model. Option D requires additional infrastructure and complexity. Option E doesn't directly address the memory issue during the training phase, although early stopping is a good practice, the underlying memory pressure will remain.

NEW QUESTION # 104
You are tasked with building a machine learning pipeline in Snowpark Python to predict customer lifetime value (CLTV). You need to access and manipulate data residing in multiple Snowflake tables and views, including customer demographics, purchase history, and website activity. To improve code readability and maintainability, you decide to encapsulate data access and transformation logic within a Snowpark Stored Procedure. Given the following Python code snippet representing a simplified version of your stored procedure:

A. The 'snowflake.snowpark.context.get_active_session()' function retrieves the active Snowpark session object, enabling interaction with the Snowflake database from within the stored procedure.
B. The replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.
C. The 'session.sql('SELECT FROM PURCHASE line executes a SQL query against the Snowflake database and returns the results as a list of Row objects.
D. The 'session.table('CUSTOMER DEMOGRAPHICS')' method creates a local Pandas DataFrame containing a copy of the data from the 'CUSTOMER DEMOGRAPHICS' table.
E. The 'session.write_pandas(df, table_name='CLTV PREDICTIONS', auto_create_table=Truey function writes the Pandas DataFrame 'df containing the CLTV predictions directly to a new Snowflake table named , automatically creating the table if it does not exist.

Answer: A,B,C,E

Explanation:
Option A is correct because is the standard method for accessing the active Snowpark session within a stored procedure. Option C is correct as the gsproc' decorator is required to register the function as a Snowpark Stored Procedure, specifying necessary packages. Option D correctly explains how to execute SQL queries using the session object and retrieve results. Option E accurately describes the function's ability to write a Pandas DataFrame to a Snowflake table and create it if it doesn't exist. Option B is incorrect because returns a Snowpark DataFrame, not a Pandas DataFrame. A Snowpark DataFrame is a lazily evaluated representation of the data, while a Pandas DataFrame is an in-memory copy.

NEW QUESTION # 105
You have successfully trained a binary classification model using Snowpark ML and deployed it as a UDF in Snowflake. The UDF takes several input features and returns the predicted probability of the positive class. You need to continuously monitor the model's performance in production to detect potential data drift or concept drift. Which of the following methods and metrics, when used together, would provide the MOST comprehensive and reliable assessment of model performance and drift in a production environment? (Select TWO)

A. Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.
B. Continuously calculate and track performance metrics like AUC, precision, recall, and Fl-score on a representative sample of labeled production data over regular intervals. Compare these metrics to the model's performance on the holdout set during training.
C. Calculate the Kolmogorov-Smirnov (KS) statistic between the distribution of predicted probabilities in the training data and the production data over regular intervals. Track any substantial changes in the KS statistic.
D. Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
E. Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.

Answer: B,C

Explanation:
Options B and D provide the most comprehensive assessment of model performance and drift. Option D, by continuously calculating key performance metrics (AUC, precision, recall, F1 -score) on labeled production data, directly assesses how well the model is performing on real- world data. Comparing these metrics to the holdout set provides insights into potential overfitting or degradation over time (concept drift). Option B, calculating the KS statistic between the predicted probability distributions of training and production data, helps to identify data drift, indicating that the input data distribution has changed. Option A can be an indicator but is less reliable than the KS statistic. Option C monitors data pipeline health, not model performance. Option E focuses on data quality, which is important but doesn't directly assess model performance drift.

NEW QUESTION # 106
......

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DSA-C03 Reliable Study Guide | Reliable DSA-C03 Test Topics

Reliable DSA-C03 Test Topics | Popular DSA-C03 Exams

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

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