DSA-C03 VALUABLE FEEDBACK - EXAM DSA-C03 LAB QUESTIONS

DSA-C03 Valuable Feedback - Exam DSA-C03 Lab Questions

DSA-C03 Valuable Feedback - Exam DSA-C03 Lab Questions

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

  • A. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
  • B. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
  • C. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
  • D. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
  • E. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.

Answer: A,B,D

Explanation:
The correct answers are B, D, and E. B: One-hot encoding allows you to see the individual effect of each neighborhood. D: ICE plots reveal how the relationship between 'location' and predicted price varies for different individual instances, highlighting potential heterogeneity. E: A two-way PDP with 'location' and 'square_footage' helps understand if the effect of location is different for houses of different sizes. Removing 'location' (option A) might decrease performance if it's a relevant feature. Replacing it with average price (option C) introduces potential bias and data leakage if the historical data is used for both training and validation.


NEW QUESTION # 12
Which of the following statements about Z-tests and T-tests are generally true? Select all that apply.

  • A. A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data.
  • B. A T-test is generally used when the sample size is large (n > 30) and the population standard deviation is known.
  • C. A T-test has fewer degrees of freedom compared to the Z-test, making it more robust to outliers.
  • D. As the sample size increases, the T-distribution approaches the standard normal (Z) distribution.
  • E. Both Z-tests and T-tests assume that the data is non-normally distributed.

Answer: A,D

Explanation:
The correct answers are A and C. A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data. As the sample size increases, the T-distribution approaches the standard normal (Z) distribution, which is a core concept in statistical inference. B is incorrect because a T-test is generally used for small sample sizes (n < 30) or when the population standard deviation is unknown. D is incorrect because both tests assume the underlying population distribution is approximately normal, especially for smaller sample sizes (though the Central Limit Theorem allows us to relax this assumption somewhat for large samples). E is incorrect because fewer degrees of freedom make the t-test less robust to outliers. Also the robustness is provided by the population distribution being approximately normal.


NEW QUESTION # 13
A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

  • A. Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
  • B. Increase the learning rate significantly to speed up convergence during training.
  • C. Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.
  • D. Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.
  • E. Reduce the number of features by randomly removing some columns, as this always prevents overfitting.

Answer: C

Explanation:
Feature scaling is crucial for Logistic Regression. Features with different scales can disproportionately influence the model's coefficients. Snowflake UDFs allow for scalable data transformation within the platform. Increasing the learning rate excessively can lead to instability. Randomly removing features can remove important information. Deep neural networks require substantial tuning and resources and aren't always the best starting point and can have issues deploying inside of Snowflake. Ignoring missing values will negatively impact performance.


NEW QUESTION # 14
You are analyzing sensor data collected from industrial machines, which includes temperature readings. You need to identify machines with unusually high temperature variance compared to their peers. You have a table named 'sensor _ readings' with columns 'machine_id', 'timestamp', and 'temperature'. Which of the following SQL queries will help you identify machines with a temperature variance that is significantly higher than the average temperature variance across all machines? Assume 'significantly higher' means more than two standard deviations above the mean variance.

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

Answer: C

Explanation:
The correct answer is A. This query first calculates the variance for each machine using a CTE (Common Table Expression). Then, it calculates the average variance and standard deviation of variances across all machines. Finally, it selects the machine IDs where the variance is more than two standard deviations above the average variance. Option B is incorrect because it tries to calculate aggregate functions within the HAVING clause without proper grouping. Option C uses a JOIN which is inappropriate in this scenario. Option D is incorrect because the window functions will not return the correct aggregate values. Option E is syntactically incorrect. QUALIFY clause should have partition BY statement.


NEW QUESTION # 15
You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

  • A. Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
  • B. Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
  • C. Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.
  • D. Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
  • E. Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.

Answer: C

Explanation:
Using a Snowflake External Function is the most efficient and secure way to interact with an external LLM API for this task. Here's why: Efficiency: External Functions allow Snowflake to directly call the external service in parallel, leveraging Snowflake's compute resources. This minimizes data transfer between Snowflake and the client environment. Security: External Functions support secure communication with external services using API integration objects, which handle authentication and authorization. Data Governance: Keeps all processing within Snowflake's secure environment, reducing the risk of data leakage. Options A, C, and E involve transferring the data outside of Snowflake, which is less secure and less performant. Option D is not a valid approach for integrating with an external LLM API.


NEW QUESTION # 16
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