Title: Die anspruchsvolle DSA-C03 echte Prüfungsfragen von uns garantiert Ihre bessere [Print This Page] Author: benbell755 Time: 2/9/2026 18:55 Title: Die anspruchsvolle DSA-C03 echte Prüfungsfragen von uns garantiert Ihre bessere P.S. Kostenlose und neue DSA-C03 Prüfungsfragen sind auf Google Drive freigegeben von EchteFrage verfügbar: https://drive.google.com/open?id=1NVPt03QdnglhteIVXq1hulkiMcXbikNi
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You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?
A. Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.
B. Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
C. Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
D. Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
E. Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
Antwort: D
Begründung:
Option D, leveraging Snowflake's native support for Streamlit, is the most appropriate and efficient approach. Streamlit allows you to build interactive web applications directly within the notebook, querying data directly from Snowflake and using Streamlit's built-in plotting capabilities (or integrating with other Python visualization libraries). This avoids pulling large amounts of data into the notebook's environment, which is crucial for large datasets. Option A is inefficient due to the data transfer overhead and limited interactivity. Option B can work but is not as streamlined as using Streamlit within the Snowflake environment. Option C will create static plots only. Option E is overly complex and less efficient than using Streamlit.
129. Frage
You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?
A. Option A
B. Option E
C. Option C
D. Option D
E. Option B
Antwort: D,E
Begründung:
Options B and D are correct because they employ techniques to mitigate overfitting. Option B uses ' RandomizedSearchCV' with cross-validation and a fixed 'random_state' , making the search reproducible and preventing overfitting by evaluating performance on multiple validation sets. Option D leverages 'BayesianSearchCV' , which uses a probabilistic model to efficiently explore the hyperparameter space, also with cross-validation and a fixed random state making search reproducible. Both methods aim to find a balance between model complexity and generalization ability. Option A is incorrect because it does not use cross-validation, which is crucial for preventing overfitting. Option C is incorrect because manual tuning without a systematic search and cross-validation is prone to bias and overfitting. Finally, option E is incorrect because while using a modern algorithm, it lacks a random state, making it difficult to reproduce the outcome.
130. 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. Partition your database by different subject matter and then query the specific partitions for your information.
C. Fine-tune the LLM on the entire knowledge base. Train a custom LLM model specifically on the knowledge base data.
D. Use Snowflake Cortex's 'COMPLETE function without any external knowledge base. Rely solely on the LLM's pre-trained knowledge.
E. 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.
Antwort: A
Begründung:
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.
131. Frage
You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?
A.
B.
C.
D. None of the above
E.
Antwort: C
Begründung:
Option A correctly addresses all requirements: Filters usage data for the last 30 days. Corrects negative values by setting them to 0 using and ' Calculates the sum of for each customer. Uses a 'LEFT JOIN' to include all customers, even those without recent usage data. Uses 'coalesce()' to set the to 0 for customers with no usage data after the join. Option B uses an ' INNER JOIN' , which would exclude customers without any recent usage data, violating the requirement to include all customers. Option C does not treat negative usage values correctly. Option D uses a "RIGHT JOIN' which would return incorrect results. Option E isn't right as option A correctly addresses all the scenarios.
132. Frage
Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?
A. Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
B. Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.
C. Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
D. Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.
E. Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.
Antwort: C,D
Begründung:
Options A and C are the most effective. Option A leverages Snowflake's native tagging capabilities combined with Snowpark for automation, allowing dynamic tagging during model deployment and retraining. Option C provides a centralized and robust metadata management approach via a third-party tool, crucial for complex model deployments requiring lineage tracking, data governance, and automated data drift monitoring. Options B and D are less efficient. Option B introduces manual and error-prone processes. Option D adds unnecessary complexity by requiring synchronization between Snowflake and an external database. While option E can be useful for generating reports, it's not a comprehensive solution for metadata tagging and model lineage tracking.
133. Frage
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