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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
A retail company is using Snowflake to store sales data'. They have a table called 'SALES DATA' with columns: 'SALE ID', 'PRODUCT D', 'SALE DATE', 'QUANTITY' , and 'PRICE'. The data scientist wants to analyze the trend of daily sales over the last year and visualize this trend in Snowsight to present to the business team. Which of the following approaches, using Snowsight and SQL, would be the most efficient and appropriate for visualizing the daily sales trend?

A. Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.
B. Export all the data from the 'SALES DATA' table to a CSV file and use an external tool like Python's Matplotlib or Tableau to create the visualization.
C. Write a SQL query that uses 'DATE TRUNC('day', SALE DATE)' to group sales by day and calculate the total sales (SUM(QUANTITY PRICE)). Use Snowsight's line chart option with the truncated date on the x-axis and total sales on the y-axis, filtering by 'SALE_DATE' within the last year. Furthermore, use moving average with window function to smooth the data.
D. Use the Snowsight web UI to manually filter the 'SALES_DATX table by 'SALE_DATE for the last year and create a bar chart showing 'SALE_ID count per day.
E. Write a SQL query that calculates the daily total sales amount CSUM(QUANTITY PRICEY) for the last year and use Snowsight's charting options to generate a line chart with 'SALE DATE on the x-axis and daily sales amount on the y-axis.

Answer: C

Explanation:
Option E provides the most efficient and appropriate solution. It uses SQL to aggregate the data by day using DATE TRUNC and calculates the total sales amount, addressing the data preparation part. Snowsight can then be used to generate a line chart, making it easy to visualize the trend over time. The usage of moving average via window functions add a layer to smooth the data so that the outliers can be removed. Other options are less efficient (exporting data to external tools) or don't directly address the visualization of trends (showing raw data in a table or manually filtering data).

NEW QUESTION # 94
A data science team at a retail company is using Snowflake to store customer transaction data'. They want to segment customers based on their purchasing behavior using K-means clustering. Which of the following approaches is MOST efficient for performing K-means clustering on a very large customer dataset in Snowflake, minimizing data movement and leveraging Snowflake's compute capabilities, and adhering to best practices for data security and governance?

A. Using a Snowflake User-Defined Function (UDF) written in Python that leverages the scikit-learn library within the UDF to perform K-means clustering directly on the data within Snowflake. Ensure the UDF is called with appropriate resource allocation (WAREHOUSE SIZE) and security context.
B. Using Snowflake's Snowpark DataFrame API with a Python UDF to preprocess the data and execute the K-means algorithm within the Snowflake environment. This approach allows for scalable processing within Snowflake's compute resources with data kept securely within the governance boundaries.
C. Employing only Snowflake's SQL capabilities to perform approximate nearest neighbor searches without implementing the full K-means algorithm. This compromises the accuracy and effectiveness of the clustering results.
D. Exporting the entire customer transaction dataset from Snowflake to an external Python environment, performing K-means clustering using scikit-learn, and then importing the cluster assignments back into Snowflake as a new table. This approach involves significant data egress and potential security risks.
E. Implementing K-means clustering using SQL queries with iterative JOINs and aggregations to calculate centroids and assign data points to clusters. This approach is computationally expensive and not recommended for large datasets. Moreover, security considerations are minimal.

Answer: B

Explanation:
Snowpark and Python UDFs provide a way to execute code within the Snowflake environment, leveraging its compute resources and keeping data within Snowflake's security and governance boundaries. This avoids data egress and is more efficient than exporting data or attempting to implement K-means directly in SQL. While B is potentially viable, D leveraging DataFrames provides further optimization. The other options are either inefficient or insecure.

NEW QUESTION # 95
You are analyzing a dataset of website traffic and conversions in Snowflake, aiming to understand the relationship between the number of pages visited CPAGES VISITED) and the conversion rate (CONVERSION_RATE). You perform a simple linear regression using the 'REGR SLOPE and 'REGR INTERCEPT functions. However, after plotting the data and the regression line, you observe significant heteroscedasticity (non-constant variance of errors). Which of the following actions, performed within Snowflake during the data preparation and feature engineering phase, are MOST appropriate to address this heteroscedasticity and improve the validity of your linear regression model? (Select all that apply)

A. Apply a Box-Cox transformation to the 'CONVERSION RATE' variable. This transformation will determine the optimal lambda value using some complex SQL statistical operations. This can be approximated to log tranformation in many real life scenarios.
B. Calculate the weighted least squares regression by weighting each observation by the inverse of the squared predicted values from an initial OLS regression. This requires multiple SQL queries.
C. Apply a logarithmic transformation to the 'CONVERSION RATE' variable using the 'LN()' function. CREATE OR REPLACE VIEW TRANSFORMED_DATA AS SELECT PAGES VISITED, LN(CONVERSION RATE) AS LOG_CONVERSION RATE FROM ORIGINAL_DATA;
D. Remove outlier data points from the dataset based on the Interquartile Range (IQR) of the residuals from the original linear regression model. This requires calculating the residuals first.
E. Standardize the 'PAGES_VISITED' and 'CONVERSION_RATE variables using the and functions.Create OR REPLACE VIEW STANDARDIZED_DATA AS SELECT (PAGES_VISITED - OVER()) / OVER() AS Z PAGES_VISITED, (CONVERSION RATE -OVER()) / OVER() AS FROM ORIGINAL_DATA;

Answer: A,C

Explanation:
Heteroscedasticity violates one of the assumptions of linear regression, leading to unreliable standard errors and potentially biased coefficient estimates. Option A (Logarithmic Transformation): Applying a logarithmic transformation to the dependent variable ('CONVERSION_RATE) is a common technique to stabilize the variance when the variance increases with the mean. This is particularly effective when the errors are proportional to the dependent variable. Option E (Box-Cox Transformation): A Box-Cox transformation is a more general approach to transforming the dependent variable to achieve normality and homoscedasticity. It estimates a parameter (lambda) that determines the optimal transformation. Log transformation is a special case of box cox transformation, where lambda = O. Option B describes weighted least squares regression, but directly implementing this within Snowflake SQL efficiently, including calculating the initial OLS regression and subsequent weights, would be complex and may not be practically feasible without Snowpark/Python integration. It's theoretically correct but challenging to implement in pure SQL. Option C, Standardization, addresses multicollinearity issues (if present) but doesn't directly tackle heteroscedasticity. It scales the variables but doesn't change the relationship between the mean and variance of the errors. Option D, outlier removal, can be a valid step in data preparation, but it's not a direct solution to heteroscedasticity. It might help reduce the impact of outliers on the model, but it doesn't address the underlying pattern of non-constant variance. Outlier treatment requires calculation of residuals first, which is not always easy, and may cause data loss, but it might indirectly reduce heteroscedasticity.

NEW QUESTION # 96
A pharmaceutical company is testing a new drug to lower blood pressure. They conduct a clinical trial with 200 patients. After treatment, the sample mean reduction in systolic blood pressure is 10 mmHg, with a sample standard deviation of 15 mmHg. You want to construct a 99% confidence interval for the true mean reduction in systolic blood pressure. Which of the following statements is most accurate concerning the appropriate distribution and critical value to use?

A. Use a chi-squared distribution with 199 degrees of freedom.
B. Use a t-distribution with 199 degrees of freedom, and the critical value is slightly larger than 2.576.
C. Use a t-distribution with 200 degrees of freedom, and the critical value is close to 2.576.
D. Use a z-distribution because we are estimating mean, and use a critical value of 1.96.
E. Use a z-distribution because the sample size is large (n > 30), and the critical value is approximately 2.576.

Answer: B

Explanation:
The correct answer is B. While the sample size is considered 'large' (n > 30), it's more accurate to use a t-distribution when the population standard deviation is unknown and estimated by the sample standard deviation. The t-distribution accounts for the added uncertainty from estimating the standard deviation. The degrees of freedom are n-1 = 199. The critical value for a 99% confidence interval with a t-distribution and 199 degrees of freedom will be slightly larger than the z-score of 2.576. Option A is incorrect because using t-distribution is slightly better. Option C is incorrect because chi-squared distribution is for variance/standard deviation. Option D is incorrect since 1.96 is z score for 95%. Option E is incorrect as the degrees of freedom should be n-1.

NEW QUESTION # 97
You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet:

You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?

A. Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.
B. Implement cross-validation at the partition level by splitting 'TRAINING_DATX into train and test sets before creating the model, and then using the 'FIT' command to train on the train set and 'PREDICT to evaluate on the test set, repeating for each partition.
C. Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.
D. Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'
E. Retrain the model with additional feature engineering to create interaction terms between 'EQUIPMENT_ID' and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in 'INPUT DATA'.

Answer: C,E

Explanation:
Options C and E are the most effective strategies. Option C (Feature Engineering): By creating interaction terms between EQUIPMENT _ ICY and other sensor features, the model can learn equipment-specific patterns. This enables the model to account for the unique characteristics of each equipment group, improving its ability to generalize across all equipment. For example, the optimal temperature threshold for triggering a failure might differ significantly between EQUIPMENT_ID' groups, and this can be captured using interaction terms. Option E (Seperate models per Equipment ID) : Hyperparameter tuning and training separate models per equipment ID enables you to optimize and customize the model specific to each equipment ID. The downsize is that we need to create and manage more models. Options A and D are less effective or may have limitations: Option A (Increase Training Data Size): While increasing the training data size can sometimes improve model performance, it doesn't guarantee that the model will learn to differentiate between the equipment groups effectively, especially if some groups have significantly different data characteristics. This can also consume a lot of resources unnecessarily. Option D (Custom cross Validation) : While it's valid, it is difficult to implement and the built in Snowflake cross validation features is much more performant and easier to use.

NEW QUESTION # 98
......

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