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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?

A. Query: 'SELECT user_segments, MEDIAN(session_duration) FROM user_behavior GROUP BY user_segments;' Visualization: Box plot showing the distribution (quartiles, median, outliers) of session duration for each user segment.
B. Query: 'SELECT user_segments, APPROX 0.25), APPROX 0.5), APPROX_PERCENTlLE(session_duration, 0.75) FROM user_behavior GROUP BY user_segments;' Visualization: Scatter plot where each point represents a user segment and the x,y coordinates represent session duration at 25th and 75th percentiles respectively.
C. Query: 'SELECT session_duration FROM user_behavior WHERE user_segments = 'New';- (repeated for each user segment). Visualization: Overlayed histograms showing the distribution of session duration for each user segment on the same axes.
D. Query: ' SELECT COUNT( ) ,user_segments FROM user_behavior GROUP BY user_segments;' Visualization: Pie chart showing proportion of each segment.
E. Query: 'SELECT user_segments, AVG(session_duration) FROM user_behavior GROUP BY Visualization: Bar chart showing average session duration for each user segment.

Answer: A

Explanation:
Using the Median (option B) provides a better central tendency measure than the average (option A) when the data may have outliers. The box plot effectively visualizes the distribution, including quartiles and outliers. Option C involves generating separate queries and histograms, which is less efficient. Calculating quantiles using 'APPROX_PERCENTILE' (Option D) is good for large datasets, but the resulting scatter plot isn't the best way to show distribution. Pie chart does not show distrubution but proportions.

NEW QUESTION # 204
You are building a time-series forecasting model in Snowflake to predict the hourly energy consumption of a building. You have historical data with timestamps and corresponding energy consumption values. You've noticed significant daily seasonality and a weaker weekly seasonality. Which of the following techniques or approaches would be most appropriate for capturing both seasonality patterns within a supervised learning framework using Snowflake?

A. Applying exponential smoothing directly to the original time series without feature engineering.
B. Decomposing the time series using STL (Seasonal-Trend decomposition using Loess) and building separate models for the trend and seasonal components, then combining the predictions.
C. Using Fourier terms (sine and cosine waves) with frequencies corresponding to daily and weekly cycles as features in a regression model.
D. Using a simple moving average to smooth the data before applying a linear regression model.
E. Creating lagged features (e.g., energy consumption from the previous hour, the same hour yesterday, and the same hour last week) and using these features as input to a regression model (e.g., Random Forest or Gradient Boosting).

Answer: C,E

Explanation:
Both creating lagged features (Option C) and using Fourier terms (Option E) are effective approaches for capturing seasonality in a supervised learning framework. Lagged features directly encode the past values of the time series, capturing the relationships and dependencies within the data. This is particularly effective when there are strong autocorrelations. Fourier terms represent periodic patterns in the data using sine and cosine waves. By including Fourier terms with frequencies corresponding to daily and weekly cycles, the model can learn to capture the seasonal variations in energy consumption. Option A is too simplistic and doesn't capture the nuances of seasonality. Option B, while valid, might be more complex to implement and maintain than Option C and E. Option D is generally less accurate than the feature engineering approaches.

NEW QUESTION # 205
A data engineer is tasked with removing duplicates from a table named 'USER ACTIVITY' in Snowflake, which contains user activity logs. The table has columns: 'ACTIVITY TIMESTAMP', 'ACTIVITY TYPE', and 'DEVICE_ID. The data engineer wants to remove duplicate rows, considering only 'USER ID', 'ACTIVITY TYPE, and 'DEVICE_ID' columns. What is the most efficient and correct SQL query to achieve this while retaining only the earliest 'ACTIVITY TIMESTAMP' for each unique combination of the specified columns?

A. Option D
B. Option E
C. Option A
D. Option B
E. Option C

Answer: D

Explanation:
Option B provides the most efficient and correct solution. - It uses the 'QUALIFY' clause along with the window function to partition the data by 'USER ID, 'ACTIVITY TYPE, and 'DEVICE ICY. Within each partition, it orders the rows by 'ACTIVITY _ TIMESTAMP' in ascending order. The function assigns a unique rank to each row within the partition. The 'QUALIFY clause filters the result set, keeping only the rows where the 'ROW NUMBER()' is equal to 1, which effectively selects the earliest activity timestamp for each unique combination of 'ACTIVITY _ TYPE , and 'DEVICE_ID'. Option A is incorrect because it aggregates and only retains the minimum 'ACTIVITY TIMESTAMP' , discarding other potentially relevant columns. Option C is incorrect because it only returns rows where a combination of 'USER_ID, ACTIVITY_TYPE, DEVICE_ID, and ACTIVITY_TIMESTAMP" appears only once, not removing duplicates based on the desired columns. Option D is incorrect because it only selects distinct combinations of USER ID, ACTIVITY_TYPE, and DEVICE_ID, thus losing the ACTIVITY_TIMESTAMP. option E is incorrect. While it keeps the ACTIVITY_TIMESTAMP as the earliest, FIRST VALUE generates all other columns based on the input data which will generate duplicates.

NEW QUESTION # 206
You are building a fraud detection model using Snowflake data'. The dataset 'TRANSACTIONS' contains billions of records and is partitioned by 'TRANSACTION DATE'. You want to use cross-validation to evaluate your model's performance on different subsets of the data and ensure temporal separation of training and validation sets. Given the following Snowflake table structure:

Which approach would be MOST appropriate for implementing time-based cross-validation within Snowflake to avoid data leakage and ensure robust model evaluation? (Assume using Snowpark Python to develop)

A. Utilize the 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with the 'input_colS argument containing 'TRANSACTION DATE'. Snowflake will automatically infer the temporal nature of the data and perform time-based cross-validation.
B. Implement a custom splitting function within Snowpark, creating sequential folds based on the 'TRANSACTION DATE column and use that with Snowpark ML's cross_validation. Ensure each fold represents a distinct time window without overlap.
C. Create a UDF that assigns each row to a fold based on the 'TRANSACTION DATE column using a modulo operation. This is then passed to the 'cross_validation' function in Snowpark ML.
D. Explicitly define training and validation sets based on date ranges within the Snowpark Python environment, performing iterative training and evaluation within the client environment before deploying a model to Snowflake. No built-in cross-validation used
E. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.

Answer: B

Explanation:
Option E is the most suitable because it explicitly addresses the temporal dependency and prevents data leakage by creating sequential, non-overlapping folds based on 'TRANSACTION DATE. Options A and D rely on potentially incorrect assumptions by Snowflake about time series data and are unlikely to provide the correct cross-validation folds. Option B can introduce leakage because it treats dates as categorical variables and performs random assignment. Option C performs the cross validation entirely outside of Snowflake, which negates the benefits of Snowflake's scalability and data proximity.

NEW QUESTION # 207
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 '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.
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 replace=True, packages=['snowflake-snowpark-python', 'pandas', decorator registers the Python function as a Snowpark Stored Procedure, allowing it to be called from SQL.

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 # 208
......

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