FREE PDF TRUSTABLE SNOWFLAKE - NEW DSA-C03 TEST MATERIALS

Free PDF Trustable Snowflake - New DSA-C03 Test Materials

Free PDF Trustable Snowflake - New DSA-C03 Test Materials

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q143-Q148):

NEW QUESTION # 143
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 D
  • B. Option B
  • C. Option C
  • D. Option E
  • E. Option A

Answer: E

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 # 144
You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

  • A. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
  • B. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
  • C. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
  • D. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
  • E. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.

Answer: D

Explanation:
Option B provides the most robust and maintainable solution. Registering each model version in the Snowflake Model Registry allows for easy tracking and rollback. Promoting the desired version to 'PRODUCTION' and dynamically fetching the model in a UDF based on this metadata ensures minimal downtime during updates and rollbacks. Option A relies on cloud storage versioning, which is less integrated with Snowflake's metadata management. Option C requires manual UDF switching, which is error-prone. Option D doesn't utilize the Model Registry effectively. Option E eliminates the benefits of version control.


NEW QUESTION # 145
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 A
  • B. Option D
  • C. Option B
  • D. Option E
  • E. Option C

Answer: D

Explanation:


NEW QUESTION # 146
You are working with a large dataset of customer transactions in Snowflake. The dataset contains columns like 'customer id' , 'transaction date', 'product category' , and 'transaction_amount'. Your task is to identify fraudulent transactions by detecting anomalies in spending patterns. You decide to use Snowpark for Python to perform time-series aggregation and feature engineering. Given the following Snowpark DataFrame 'transactions_df , which of the following approaches would be MOST efficient for calculating a 7-day rolling average of for each customer, while also handling potential gaps in transaction dates?

  • A. Use 'window.partitionBy('customer_id').orderBy('transaction_date').rowsBetween(-6, Window.currentRow)' within a 'select' statement and handle any missing dates using 'fillna()' after calculating the rolling average.
  • B. Use a Snowpark Pandas UDF to calculate the rolling average for each customer after collecting all transactions for that customer into a Pandas DataFrame. Handle missing dates using Pandas functionality.
  • C. Use a stored procedure in SQL to iterate over each customer, calculate the rolling average using a cursor and conditional logic for handling missing dates.
  • D. Use'window.partitionBy('customer_id').orderBy('transaction_date').rangeBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with a date range table joined to the transactions, filling in missing days before calculating the rolling average with 'transaction_amount' set to 0 for the inserted days.
  • E. Use a simple followed by a UDF to calculate the rolling average. Fill in missing dates manually within the UDF.

Answer: D

Explanation:
Option E is the MOST efficient. Using a date range table joined with the transactions DataFrame to fill in missing dates before calculating the rolling average using 'rangeBetween' is more performant than options that involve UDFs or procedural logic. Options A, C, and D introduce overhead with UDFs or stored procedures which can be slow for large datasets. Option B is less flexible in handling missing dates because 'rowsBetween' considers only the row number, not the actual date difference, potentially leading to inaccurate averages when there are gaps in dates.


NEW QUESTION # 147
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. 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.
  • D. 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.
  • E. 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.

Answer: C

Explanation:
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.


NEW QUESTION # 148
......

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