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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q72-Q77):
NEW QUESTION # 72
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?
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 # 73
A data scientist needs to analyze website session data stored in a Snowflake table named 'WEB SESSIONS'. The table contains columns like 'SESSION D', 'USER_ID, 'PAGE_VIEWS', 'TIME SPENT_SECONDS', and 'TIMESTAMP. They want to identify potential bot traffic by analyzing the correlation between 'PAGE VIEWS' and 'TIME SPENT SECONDS'. Which of the following Snowflake SQL queries is the MOST efficient and statistically sound way to calculate the Pearson correlation coefficient between these two columns, handling potential NULL values appropriately?
Answer: B
Explanation:
The 'CORR function in Snowflake directly calculates the Pearson correlation coefficient and implicitly handles NULL values by excluding rows where either input is NULL. Option A is incorrect because it does not explicitly filter NULL values, though the 'CORR' function itself handles it, Option B is mathematically correct but less concise. Option C uses 'APPROX CORR, which is useful for large datasets where approximate results are acceptable, but for a general scenario without size constraints, 'CORR is preferred for accuracy. While Option E correctly calculates the correlation coefficient using covariance and standard deviation, it uses approximation functions which may impact accuracy without a necessary tradeoff.
NEW QUESTION # 74
A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?
Answer: A
Explanation:
Option D is the most efficient and accurate because it uses a single SQL query to calculate the MSE directly. It avoids using cursors or procedural logic, which are less performant in Snowflake. It uses SUM to calculate the sum of squared errors and COUNT( ) to get the total number of records, then divides to obtain the average (MSE). Option B calculates the average of power, that is wrong mathematical operation, Option A is correct from mathematical point but slow because of cursor and not following Snowflake best practices, option C is using JavaScript which is also valid, but Snowflake recommends to use SQL when possible for performance, and option E is using external python for model calculation, that not best for this scenarios.
NEW QUESTION # 75
You are developing a real-time fraud detection system using Snowpark and deploying it as a Streamlit application connected to Snowflake. The system ingests transaction data continuously and applies a pre-trained machine learning model (stored as a binary file in Snowflake's internal stage) to score each transaction for fraud. You need to ensure the model loading process is efficient, and you're aiming to optimize performance by only loading the model once when the application starts, not for every single transaction. Which combination of approaches will BEST achieve this in a reliable and efficient manner, considering the Streamlit application's lifecycle and potential concurrency issues?
Answer: C
Explanation:
Option A is the best approach. 'st.cache_data' is the recommended way to cache data in Streamlit, including large objects like machine learning models. It automatically handles concurrency and ensures the model is only loaded once per Streamlit application instance. Because it's Streamlit's mechanism, it plays well with the Streamlit lifecycle. It is not required to use a Pandas DataFrame like option C suggests. Python global variables (B) are not suitable for web apps due to concurrency issues. While threading locks (D) could work, they add complexity and are generally less desirable than using Streamlit's caching mechanism. The model loading can be cached without a try-except block to set the Snowflake session as a singleton (E).
NEW QUESTION # 76
You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?
Answer: C,D
Explanation:
Options A and E are the most effective because they provide a quantitative and statistically sound way to measure data drift. Calculating the KS statistic (Option A) for each feature allows you to identify which features have drifted the most. Calculating Jensen-Shannon Divergence on the predicted probability distributions will tell how much the prediction patterns have changed in the newer data, which helps in assesing drift. Option B is manual and subjective. Option C might lead to model instability without understanding the nature of the drift. Option D, while helpful for initial exploration, might not be sensitive enough to detect subtle but important drifts. Option E provides insight specifically into the model's output behavior shifts.
NEW QUESTION # 77
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