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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
You are tasked with identifying fraudulent transactions from unstructured log data stored in Snowflake. The logs contain various fields, including timestamps, user IDs, and transaction details embedded within free-text descriptions. You plan to use a supervised learning approach, having labeled a subset of transactions as 'fraudulent' or 'not fraudulent.' Which of the following methods best describes the extraction and processing of this data for training a machine learning model within Snowflake?

A. Export the entire log data to an external machine learning platform (e.g., AWS SageMaker) and perform feature extraction, NLP processing, and model training there. Import the trained model back into Snowflake as a UDF for prediction.
B. Treat the unstructured log description as a categorical feature and directly apply one-hot encoding within Snowflake, then train a classification model. Due to high dimensionality perform PCA for dimensionality reduction before training.
C. Use a combination of regular expressions and natural language processing (NLP) techniques within Snowflake UDFs to extract key features such as transaction amounts, product categories, and sentiment scores from the log descriptions. Then, combine these extracted features with other structured data (e.g., user demographics) and train a classification model using these features. The NLP steps include tokenization, stop word removal, and TF-IDF vectorization.
D. Extract the entire log description field and train a word embedding model (e.g., Word2Vec) on the entire dataset. Average the word vectors for each transaction's log description to create a document vector. Train a classification model (e.g., Random Forest) on these document vectors within Snowflake.
E. Use regular expressions within a Snowflake UDF to extract relevant information (e.g., amount, item description) from the log descriptions. Convert extracted data into numerical features using one-hot encoding within the UDF. Then, train a model using the extracted numerical features directly within Snowflake using SQL extensions for machine learning.

Answer: C

Explanation:
Option C provides the most comprehensive and effective approach. It combines the strengths of both regular expressions (for structured data extraction) and NLP techniques (for understanding the semantic content of the log descriptions). Using Snowflake UDFs keeps the data processing within Snowflake, minimizing data movement. Combining extracted features with other structured data enhances the model's performance.

NEW QUESTION # 42
You are building a churn prediction model for a telecommunications company using Snowflake and Snowpark ML. You have trained a Gradient Boosting Machine (GBM) model and want to understand the feature importance to identify key drivers of churn. You've used SHAP (SHapley Additive exPlanations) values to explain individual predictions. Given a customer with a high churn risk, you observe that the 'monthly_charges' feature has a significantly large negative SHAP value for that specific prediction. Which of the following statements best interprets this observation in the context of feature impact?

A. Increasing 'monthly_charges' for this customer is likely to increase their probability of churning.
B. Increasing 'monthly_charges' for this customer is likely to decrease their probability of churning.
C. The negative SHAP value indicates that 'monthly_charges' is negatively correlated with all customers' churn probability, irrespective of their individual profile.
D. The 'monthly_charges' feature has no impact on the customer's churn probability.
E. The negative SHAP value suggests 'monthly_charges' interacts with other features. Its precise impact is conditional and cannot be generalized without further analysis of feature interaction effects with SHAP values.

Answer: A

Explanation:
A negative SHAP value for a specific prediction indicates that the feature's value pushed the prediction lower compared to the average prediction. In the context of churn, a lower prediction means a higher probability of churning. Thus, an increase in 'monthly_charges' for this specific customer, given their other features, is likely to increase their churn probability. Option E is partially correct as feature interactions are important but B is the best immediate interpretation.

NEW QUESTION # 43
You are analyzing website clickstream data stored in Snowflake to identify user behavior patterns. The data includes user ID, timestamp, URL visited, and session ID. Which of the following unsupervised learning techniques, combined with appropriate data transformations in Snowflake SQL, would be most effective in discovering common navigation paths followed by users? (Choose two)

A. Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.
B. Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.
C. DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.
D. Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.
E. K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.

Answer: D,E

Explanation:
Sequence clustering is appropriate for identifying navigation paths because it considers the order of URLs visited within a session. Using Snowflake's LISTAGG function allows for creating the required sequential data. K-Means clustering can also be effective if relevant features are engineered from the URL data (e.g., frequency of visiting specific domains). Association rule mining is less suitable for identifying navigation paths as it focuses on co-occurrence rather than sequence. PCA followed by hierarchical clustering and DBSCAN are not well-suited for identifying sequential navigation paths from clickstream data. Option 'A' is incorrect because association rule mining directly on raw URL data is unlikely to be effective without prior sequence extraction. Option 'D' and 'E' are not suitable for this type of analysis.

NEW QUESTION # 44
You have a table 'PRODUCT SALES in Snowflake with columns: 'PRODUCT (INT), 'SALE_DATE (DATE), 'SALES_AMOUNT (FLOAT), and 'PROMOTION FLAG' (BOOLEAN). You need to perform the following data preparation steps using Snowpark SQLAPI:

A. Converting 'SALE_DATE to a quarterly representation (e.g., '2023-QI').
B. Creating a new feature representing the percentage change in 'SALES_AMOUNT compared to the previous day for the same 'PRODUCT_ID. Handle the first day of each 'PRODUCT by setting 'SALES_GROWTH' to O.
C. Creating a feature that returns 1 if there is a PROMOTION_FLAG of True and SALES_AMOUNT > 1000, and zero otherwise
D. Handling missing 'SALES_AMOUNT values by imputing them with the average 'SALES_AMOUNT' for the same 'PRODUCT_ID during the previous month. If there's no data for the previous month, use the overall average for that
E. All of the above.

Answer: E

Explanation:
All the described data preparation steps (A, B, C, and D) are common and relevant in feature engineering for time-series or sales data analysis. Imputing missing values using rolling averages, converting dates to categorical representations, calculating growth rates, and using flag-based transformations are all standard practices. The use of 'LEAD or 'LAG' window functions is essential for calculating , and handling edge cases (like the first day of a product's sales) is crucial for data integrity. A 'CASE statement or similar construct would be needed for the PROMOTION FLAG logic.

NEW QUESTION # 45
You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

A. Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
B. Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.
C. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
D. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
E. Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.

Answer: A,B

Explanation:
Explanation:Options B and D are the most appropriate and efficient solutions for handling large datasets when calculating rolling statistics with Snowpark Pandas. Option B uses the 'window' function in Snowpark SQL. Leverage the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Option D uses Snowpark's Pandas UDFs. Snowpark's Pandas UDFs with vectorization allow you to bring the processing logic to the data within Snowflake, avoiding the need to move the entire dataset to the client-side and bypassing memory limitations. This approach is generally more scalable and performant for large datasets. Option A is inefficient as it retrieves groups of data from Snowflake to client side before creating the calculations before sending back to snowflake. Option C is correct but complex and not optimal. Option E is possible, but it's not a scalable solution and can be costly.

NEW QUESTION # 46
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