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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q200-Q205):

NEW QUESTION # 200
A marketing team is using Snowflake to store customer data including demographics, purchase history, and website activity. They want to perform customer segmentation using hierarchical clustering. Considering performance and scalability with very large datasets, which of the following strategies is the MOST suitable approach?

A. Employ BIRCH clustering with Snowflake Python UDF. Configure Snowflake resources accordingly. Optimize the clustering process. And tune parameters.
B. Randomly sample a small subset of the customer data and perform hierarchical clustering on this subset using an external tool like R or Python with scikit-learn. Assume that results generalize well to the entire dataset. Avoid using Snowflake for this purpose.
C. Perform mini-batch K-means clustering using Snowflake's compute resources through a Snowpark DataFrame. Take a large sample of each mini-batch and perform hierarchical clustering on each mini-batch and then create clusters of clusters.
D. Utilize a SQL-based affinity propagation method directly within Snowflake. This removes the need for feature scaling and specialized hardware.
E. Directly apply an agglomerative hierarchical clustering algorithm with complete linkage to the entire dataset within Snowflake, using SQL. This is computationally feasible due to SQL's efficiency.

Answer: A

Explanation:
Hierarchical clustering has a high time complexity, making it impractical for large datasets. While mini-batch K-means provides the most efficient option for large datasets. BIRCH is more suited for huge datasets and can be applied as a Snowflake Python UDF with Snowpark DataFrames to provide scalability and high performance as its better than other clustering such as affinity propagation. Options A and E are impractical due to the computational cost of hierarchical clustering in SQL or affinity propagation in SQL. Sampling (Option C) can lead to inaccurate results.

NEW QUESTION # 201
You are using Snowpark to build a collaborative filtering model for product recommendations. You have a table 'USER_ITEM INTERACTIONS with columns 'USER ID', 'ITEM ID', and 'INTERACTION TYPE'. You want to create a sparse matrix representation of this data using Snowpark, suitable for input into a matrix factorization algorithm. Which of the following code snippets best achieves this while efficiently handling large datasets within Snowflake?

Answer: A

Explanation:
Option B is the most efficient and scalable approach. It utilizes Snowpark's 'pivot' function for efficient aggregation and sparse matrix creation directly within Snowflake's engine. Option A pulls the entire dataset into pandas, which is inefficient for large datasets. Options C and D are not complete as option C does not correctly populate values in the sparse matrix while creating empty matrix without any values , and D attempts a cross join which can lead to extreme performance issues. Option E, although similar to B, is incorrect because the pivot() function does not accept the values parameter. It's already present in agg function.

NEW QUESTION # 202
You are a data scientist working for a retail company using Snowflake. You're building a linear regression model to predict sales based on advertising spend across various channels (TV, Radio, Newspaper). After initial EDA, you suspect multicollinearity among the independent variables. Which of the following Snowflake SQL statements or techniques are MOST appropriate for identifying and addressing multicollinearity BEFORE fitting the model? Choose two.

A. Implement Principal Component Analysis (PCA) using Snowpark Python to transform the independent variables into uncorrelated principal components and then select only the components explaining a certain percentage of the variance.
B. Drop one of the independent variable randomly if they seem highly correlated.
C. Calculate the Variance Inflation Factor (VIF) for each independent variable using a user-defined function (UDF) in Snowflake that implements the VIF calculation based on R-squared values from auxiliary regressions. This requires fitting a linear regression for each independent variable against all others.
D. Use ' on each independent variable to estimate its uniqueness. If uniqueness is low, multicollinearity is likely.
E. Generate a correlation matrix of the independent variables using 'CORR aggregate function in Snowflake SQL and examine the correlation coefficients. Values close to +1 or -1 suggest high multicollinearity.

Answer: C,E

Explanation:
Multicollinearity can be identified by calculating the VIF for each independent variable. VIF is calculated by regressing each independent variable against all other independent variables and calculating 1/(1-RA2), where RA2 is the R-squared value from the regression. A high VIF suggests high multicollinearity. Correlation matrices generated with 'CORR can also reveal multicollinearity by showing pairwise correlations between independent variables. PCA using Snowpark is also a viable option, but less direct than VIF and correlation matrix analysis for identifying multicollinearity. APPROX_COUNT_DISTINCT is not directly related to identifying multicollinearity. Randomly dropping variables will also lead to data loss.

NEW QUESTION # 203
A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

Answer: C,D

Explanation:
Options B and C correctly calculates the standard deviation. Option B utilizes 'NVL' , which is the equivalent of 'COALESCE or ' IFNULL', to handle NULL Customer Segment values, and 'STDDEV_SAMP' for sample standard deviation, which is generally the correct function to use when dealing with a sample of the entire population. Option C also uses 'COALESCE and utilizes the 'STDDEV POP function, which returns the population standard deviation, assuming the data represents the whole population. Option A uses IFNULL, which works, and STDDEV, which is an alias for either STDDEV SAMP or STDDEV POP. The exact behavior will depend on session variable setting. Option D also uses 'CASE WHEN' construct which works to identify Unknown segments. STDDEV is again aliased. Option E calculates the variance and not Standard deviation.

NEW QUESTION # 204
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. Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.
B. Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'
C. Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.
D. 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'.
E. 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.

Answer: A,D

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 # 205
......

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