EXCEL IN THE CERTIFICATION EXAM WITH REAL SNOWFLAKE DSA-C03 QUESTIONS

Excel in the Certification Exam With Real Snowflake DSA-C03 Questions

Excel in the Certification Exam With Real Snowflake DSA-C03 Questions

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q240-Q245):

NEW QUESTION # 240
You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

  • A. Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
  • B. Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
  • C. Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
  • D. Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
  • E. Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.

Answer: B,E

Explanation:
Option B is correct because breaking down the transformations into smaller Dynamic Tables with tailored refresh intervals ensures that only necessary data is recomputed, minimizing cost and resource usage. Option D is also correct because combining Dynamic Tables for feature engineering with Snowpark ML allows for efficient model training and deployment within Snowflake, leveraging the platform's scalability and security features. Furthermore, specifying refresh intervals based on data freshness guarantees that the model is trained on up-to-date information. Option A could lead to unnecessary computations if the entire table needs to be refreshed even for minor changes. Option C relies on traditional tables and stored procedures, which may be less efficient and harder to manage than Dynamic Tables. Option E ignores data freshness, which can significantly impact model accuracy.


NEW QUESTION # 241
You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

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

Answer: B

Explanation:


NEW QUESTION # 242
You have a regression model deployed in Snowflake predicting customer churn probability, and you're using RMSE to monitor its performance. The current production RMSE is consistently higher than the RMSE you observed during initial model validation. You suspect data drift is occurring. Which of the following are effective strategies for monitoring, detecting, and mitigating this data drift to improve RMSE? (Select TWO)

  • A. Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.
  • B. Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.
  • C. Implement a process to continuously calculate and track the RMSE on a holdout dataset representing the most recent data, alerting you when the RMSE exceeds a predefined threshold.
  • D. Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.
  • E. Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.

Answer: B,C

Explanation:
Option A provides a proactive approach to monitoring the model's performance on new data and triggering alerts when the RMSE deteriorates. Option C helps identify changes in the input data that could be causing the drift. Option B is not ideal, as retraining on all historical data might not effectively adapt to recent drifts. Option D is inefficient and impractical for large datasets. Option E is incorrect because a high RMSE indicates poor performance and warrants investigation, not ignoring.


NEW QUESTION # 243
You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: E

Explanation:
Option E is correct. It uses "F.avg' for calculating the average, selects only the required columns ('customer_id', 'average_transaction_amount_last_7_days') in and then sets on to ensure the feature group is fully materialized before proceeding. 'blocking-True' is important for production pipelines to avoid race conditions.


NEW QUESTION # 244
You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

  • A. Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
  • B. Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
  • C. Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
  • D. Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
  • E. Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.

Answer: A,B,C,E

Explanation:
Options A, B, C and E are all valid methods. Calling external APIs (A) requires network policy configuration. Loading data into tables (B) is a standard approach. Using external functions (C) allows pre-processing. Option D is highly impractical for large or frequently updated datasets. Snowflake Pipes (E) are an effective way to continually ingest external data into Snowflake.


NEW QUESTION # 245
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