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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
You are working with a large dataset in Snowflake and need to build a machine learning model using scikit-learn in Python. You want to leverage Snowflake's compute resources for feature engineering to speed up the process. Which of the following approaches correctly combines Snowflake's SQL capabilities with scikit-learn for feature engineering and model training, while minimizing data transfer between Snowflake and the Python environment?

A. Create Snowflake User-Defined Functions (UDFs) in Python for complex feature engineering calculations. Call these UDFs within a SQL query to apply the feature engineering to the Snowflake data. Load the resulting features into a Pandas DataFrame and train the scikit-learn model.
B. Use the Snowflake Python Connector to execute individual SQL queries for each feature engineering step. Load the resulting features step-by-step into a Pandas DataFrame and train the scikit-learn model.
C. Write a complex SQL query in Snowmake to perform all feature engineering, then load the resulting features into a Pandas DataFrame and train the scikit-learn model.
D. Implement the feature engineering steps directly in Python using Pandas and scikit-learn, then load the raw data into a Pandas DataFrame and apply the transformations. Finally, train the scikit-learn model.
E. Use Snowflake external functions to invoke a remote service (e.g., AWS Lambda) for feature engineering. Pass data from Snowflake to the remote service, receive the engineered features back, and load them into a Pandas DataFrame for model training.

Answer: A

Explanation:
Option D is the most efficient approach. Using Snowflake UDFs in Python allows you to perform complex feature engineering directly within Snowflake's compute environment, minimizing the amount of data that needs to be transferred to the Python environment. This reduces network latency and improves performance. Option A may be workable but it would need writing complex SQL queries. Option B will involve a lot of individual interactions between Snowflake and python making this a slower and more complex process. Option C would bring the data out to python before processing it with Pandas and scikit-learn, meaning you'd lose out on the compute of Snowflake. Option E is a viable solution to offload compute to a different compute environment than the python environment and load into a Pandas DataFrame.

NEW QUESTION # 157
You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

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

Answer: B,C

Explanation:
Option C and D demonstrate the most efficient approaches using Snowpark DataFrame operations and window functions. Option B is highly inefficient due to the use of UDFs and looping.Option E mixes pandas and snowpark operations which requires intermediate conversion of data into dataframe and it is not recommended for large datasets and is not aligned with Snowpark best practices. Option A just presents the base code and not a solution.

NEW QUESTION # 158
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?

A. Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
B. Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
C. Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
D. Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
E. Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.

Answer: B,E

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 # 159
A data scientist is analyzing website click-through rates (CTR) for two different ad campaigns. Campaign A ran for two weeks and had 10,000 impressions with 500 clicks. Campaign B also ran for two weeks with 12,000 impressions and 660 clicks. The data scientist wants to determine if there's a statistically significant difference in CTR between the two campaigns. Assume the population standard deviation is unknown and unequal for the two campaigns. Which statistical test is most appropriate to use, and what Snowflake SQL code would be used to approximate the p-value for this test (assume 'clicks_b' , and are already defined Snowflake variables)?

A. Az-test, because we know the population standard deviation. Snowflake code: 'SELECT normcdf(clicks_a/impressions_a - clicks_b/impressions_b, O, 1)'
B. An independent samples t-test, because we are comparing the means of two independent samples. Snowflake code: SELECT
C. An independent samples t-test (Welch's t-test), because we are comparing the means of two independent samples with unequal variances. Snowflake code (approximation using UDF - assuming UDF 'p_value_from_t_stat' exists that calculates p-value from t-statistic and degrees of freedom):
D. A one-sample t-test, because we are comparing the sample mean of campaign A to the sample mean of campaign Snowflake code: 'SELECT t_test_lsamp(clicks_a/impressions_a - clicks_b/impressions_b, 0)'
E. A paired t-test, because we are comparing two related samples over time. Snowflake code: 'SELECT t_test_ind(clicks_a/impressions_a, 'VAR EQUAL-TRUE')

Answer: B

Explanation:
The correct answer is E. Since we're comparing the means of two independent samples (Campaign A and Campaign B) and the population standard deviations are unknown, an independent samples t-test is appropriate. Because the problem stated that the variances are unequal, Welch's t-test provides a more accurate p-value and confidence intervals. The Snowflake function handles independent samples and the 'VAR_EQUAL=FALSE' parameter specifies that the variances should not be assumed to be equal. The other options are incorrect because they use inappropriate tests given the problem conditions. The z-test is not appropriate because the population standard deviations are unknown. A paired t-test is for related samples. A one sample test is to test one mean against a constant not another mean.

NEW QUESTION # 160
You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

A. Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
B. Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.
C. Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.
D. Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
E. Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.

Answer: A,B,E

Explanation:
Options B, C, and D offer the most effective strategies for optimizing performance when training a model incrementally with a Python UDTF in Snowflake. Batching updates (B) reduces the overhead of model updates. Persisting the model to a Snowflake stage (C) decouples training from inference and allows for model reuse. Offloading training to an external function (D) leverages dedicated ML infrastructure. Caching (A) might offer some marginal improvement but is unlikely to address the core performance bottleneck. While Java or Scala (E) can be faster than Python, rewriting the UDTF is a significant undertaking and might not be necessary if other optimization strategies are applied effectively. Also the question is specific about Python. In summary, consider batching and persistence as key in performance optimization.

NEW QUESTION # 161
......

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