2025 SNOWFLAKE DSA-C03: SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM AUTHORITATIVE EXAM DISCOUNT VOUCHER

2025 Snowflake DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Authoritative Exam Discount Voucher

2025 Snowflake DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Authoritative Exam Discount Voucher

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q269-Q274):

NEW QUESTION # 269
You have successfully deployed a machine learning model in Snowflake using Snowpark and are generating predictions. You need to implement a robust error handling mechanism to ensure that if the model encounters an issue during prediction (e.g., missing feature, invalid data type), the process doesn't halt and the errors are logged appropriately. You are using a User-Defined Function (UDF) to call the model. Which of the following strategies, when used IN COMBINATION, provides the BEST error handling and monitoring capabilities in this scenario?

  • A. Implement a custom logging solution by writing error messages to an external file storage (e.g., AWS S3) using an external function called from within the UDE
  • B. Wrap the prediction call in a 'SYSTEM$QUERY_PROFILE function to get detailed query execution statistics and identify potential performance bottlenecks.
  • C. Use Snowflake's event tables to capture errors and audit logs related to the UDF execution.
  • D. Rely solely on Snowflake's query history to identify failed predictions and debug the model, without any explicit error handling within the UDE
  • E. Use a 'TRY...CATCH' block within the UDF to catch exceptions, log the errors to a separate Snowflake table, and return a default prediction value (e.g., NULL) for the affected row.

Answer: C,E

Explanation:
The combination of A and D provides the best error handling and monitoring. A 'TRY...CATCH' block within the UDF allows for graceful handling of exceptions and prevents the entire process from failing. Logging errors to a separate Snowflake table allows for easy analysis and debugging. Returning a default value ensures that downstream applications don't encounter unexpected errors due to missing predictions. Snowflake's event tables capture a broader range of errors and audit logs, providing a comprehensive view of the UDF's execution. Option B is insufficient as it relies solely on post-mortem analysis. Option C is useful for performance profiling but doesn't address error handling directly. Option E introduces external dependencies and complexity when a native Snowflake solution is available and potentially introduces latency in the prediction process. It also can impact costs since you are using external function to copy the logs outside snowflake, where cost will be charged.


NEW QUESTION # 270
A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

  • A. Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.
  • B. Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.
  • C. Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.
  • D. Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
  • E. Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.

Answer: D

Explanation:
MLflow integration (B) within Snowpark provides a streamlined and integrated solution for model logging and artifact management, minimizing operational overhead by directly tracking experiments, logging parameters/metrics, and storing artifacts within Snowflake stages or external storage. Other options involve more manual work or introduce dependencies on external platforms, increasing complexity and management overhead.


NEW QUESTION # 271
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. 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.
  • C. 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.
  • D. 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.
  • E. 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.

Answer: B,D

Explanation:
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.


NEW QUESTION # 272
A data scientist is developing a fraud detection model using Snowpark ML on Snowflake. They have a feature engineering pipeline implemented as a Snowpark DataFrame transformation. The pipeline includes several complex UDFs. The data scientist observes that the pipeline execution is slow. What are the most effective techniques to optimize the feature engineering pipeline's performance in Snowpark?

  • A. Replace Python UDFs with Snowflake SQL UDFs where possible, as SQL UDFs often offer better performance due to Snowflake's optimization capabilities.
  • B. Disable Snowpark's lazy evaluation by executing on the DataFrame after each transformation.
  • C. Rewrite Python UDFs as vectorized Python UDFs using the 'pandas' API within Snowpark to leverage batch processing.
  • D. Cache intermediate DataFrames using or 'persist()' to avoid recomputation of common transformations.
  • E. Reduce the size of the input DataFrame by sampling the data.

Answer: A,C,D

Explanation:
Caching intermediate results (B) prevents redundant calculations. Vectorized Python UDFs (C) using pandas enhance performance by processing data in batches. Snowflake SQL UDFs (E) can often outperform Python UDFs due to Snowflake's internal optimizations. Sampling (A) might reduce accuracy. Disabling lazy evaluation (D) negates the benefits of Snowpark's query optimization.


NEW QUESTION # 273
You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting model, you want to understand the relationship between 'number_of_products' and the churn probability. You generate a partial dependence plot (PDP) for 'number_of_products'. The PDP shows a steep increase in churn probability as 'number_of_products' increases from 1 to 3, followed by a plateau. Which of the following statements are the MOST accurate interpretations of this PDP? Assume the dataset is balanced and has undergone proper preprocessing.

  • A. Increasing the number of products purchased by all customers will definitively reduce overall churn.
  • B. There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.
  • C. Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.
  • D. The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.
  • E. The model is perfectly calibrated, and the PDP accurately represents the true causal effect of 'number_of_products' on churn.

Answer: B,C

Explanation:
The correct answers are A and C. A: The plateau after 3 products indicates that increasing purchases beyond this point doesn't significantly reduce churn. C: PDPs show correlation, not causation. A confounding variable could be driving both 'number_of_products' and churn. Option B is incorrect because no model is perfectly calibrated and PDPs don't represent causal effects without further analysis. Option D is plausible but requires more information about the specific model and feature interactions. Option E is incorrect as PDPs indicate correlation and not necessarily causation, thus, it would be unsafe to assume increasing the number of products would definitively reduce churn.


NEW QUESTION # 274
......

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