Title: DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Dumps & PassGui [Print This Page] Author: paullan108 Time: yesterday 11:15 Title: DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Dumps & PassGui BONUS!!! Laden Sie die vollständige Version der Zertpruefung DSA-C03 Pr¨¹fungsfragen kostenlos herunter: https://drive.google.com/open?id=1iVGspPYDae2M-iV5GoggJCqbsbAfFiom
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You are building an automated model retraining pipeline for a sales forecasting model in Snowflake using Snowflake Tasks and Stored Procedures. After retraining, you want to validate the new model against a champion model already deployed. You need to define a validation strategy using the following models: champion model deployed as UDF "FORECAST UDF , and contender model deployed as UDF 'FORECAST UDF NEW'. Given the following objectives: (1) Minimal impact on production latency, (2) Ability to compare predictions on a large volume of real-time data, (3) A statistically sound comparison metric. Which of the following SQL statements best represents how to efficiently compare the forecasts of the two models on a sample dataset and calculate the Root Mean Squared Error (RMSE) to validate the new model?
A.
B.
C.
D.
E.
Antwort: E
Begr¨¹ndung:
Option E is the best approach. It samples the data using 'SAMPLE BERNOULLI(IO)' for minimal impact on production. Then, it calculates both the challenger RMSE (new model) and the champion RMSE on this sample data. This provides a direct comparison of the model performance against actual sales and also allows to minimise runtime to compute this metric compared to option C which computes a difference without evaluating if the new model has a better score. Sampling helps with minimal impact while comparison metric in this case needs the actual_sales column. This provides a statistically relevant comparison within Snowflake, minimizing external processing. Option A does not compare the model to the ground truth (actual sales). Option B only compares the challenger and champion models' predictions against each other on a small, limited dataset (1000 records), which may not be representative. Options C calculates the RMSE difference directly and has a SAMPLE size of 1, which is unlikely to reflect the reality and Option D filters based on RMSE, which makes the approach bias and makes it harder to evalute if the RMSE is statistically significant.
22. Frage
You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.
A. Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
B. Increase the size of the training dataset significantly by querying data from external sources.
C. Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
D. Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.
E. Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
Antwort: C,D,E
Begr¨¹ndung:
Options A, B, and D are the most effective strategies for addressing multicollinearity and overfitting in this scenario. Ridge Regression (A) adds an L2 regularization term, which penalizes large coefficients and reduces overfitting. Manually removing highly correlated features (B) addresses multicollinearity directly. Performing feature scaling (D) ensures that features with different scales do not disproportionately influence the model. Increasing training data (C) is generally helpful, but doesn't directly solve multicollinearity. Decreasing MAX ITERATIONS (E) might prevent the model from fully converging, but is a less targeted approach than regularization or feature selection.
23. Frage
A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.
A. Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.
B. Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.
C. Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.
D. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.
E. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
Antwort: D
Begr¨¹ndung:
Option C provides the most robust and scalable approach. Using Snowflake external functions allows leveraging pre-trained models without moving the data out of Snowflake. Applying sentiment analysis and topic modeling generates features that can be used by a supervised classification model trained on a labeled subset of reviews. This combines the power of external models with Snowflake's data processing capabilities. Using labeled data allows for better segment definition using Supervised approach.
24. Frage
You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?
A. Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
B. Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.
C. Force single-threaded execution by setting to avoid overhead associated with parallel processing.
D. Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
E. Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
Antwort: A,E
Begr¨¹ndung:
Vectorized UDFs and SQL Views are the key to optimizing data pre-processing. Options B and E are correct. B - Utilize Snowpark's vectorized UDFs and DataFrame operations: Snowpark is designed to push computation down to Snowflake's distributed compute engine. Vectorized UDFs allow you to execute Python code in a parallel and efficient manner directly within Snowflake. E - SQL View: Snowpark DataFrame API can query the view from SQL directly. Writing the data preparation logic in SQL leverages the snowflake's engine more effectively than Pandas or Python on a client machine. Options A, C, and D are generally incorrect: Option A is incorrect as it defeats the purpose of using Snowpark. Parallel execution is generally much faster. Option C is incorrect as moving data outside of snowflake is costly. Option D is incorrect. Snowpark is designed to manage a large scale of data.
25. Frage
You are using Snowflake Cortex to build a customer support chatbot that leverages LLMs to answer customer questions. You have a knowledge base stored in a Snowflake table. The following options describe different methods for using this knowledge base in conjunction with the LLM to generate responses. Which of the following approaches will likely result in the MOST accurate, relevant, and cost-effective responses from the LLM?
A. Use Retrieval-Augmented Generation (RAG). Generate vector embeddings for the knowledge base entries, perform a similarity search to find the most relevant entries for each customer question, and include those entries in the prompt.
B. Directly prompt the LLM with the entire knowledge base content for each customer question. Concatenate all knowledge base entries into a single string and include it in the prompt.
C. Partition your database by different subject matter and then query the specific partitions for your information.
D. Use Snowflake Cortex's 'COMPLETE function without any external knowledge base. Rely solely on the LLM's pre-trained knowledge.
E. Fine-tune the LLM on the entire knowledge base. Train a custom LLM model specifically on the knowledge base data.
Antwort: A
Begr¨¹ndung:
RAG (Retrieval-Augmented Generation) is the most effective approach (C). It combines the benefits of LLMs with the ability to incorporate external knowledge. Prompting with the entire knowledge base (A) is inefficient and might exceed context limits. Relying solely on the pre-trained LLM (B) won't leverage your specific knowledge base. Fine-tuning (D) is expensive and requires significant effort and only parititioning (E) won't help.
26. Frage
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