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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q237-Q242):

NEW QUESTION # 237
A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A. Add '@F.sproc' decorator before the function definition.
B. Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),
C. Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.
D. Redefine the function to accept string arguments and cast them to the correct data types within the function.
E. Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.

Answer: B

Explanation:
The error message 'UDFArgumentException: Invalid argument types for function 'calculate_engagement_score_udf. Expected arguments: ONT, FLOAT, INT], actual arguments: [COLUMN_NAME, COLUMN_NAME, COLUMN_NAME]' indicates that the UDF is receiving column objects instead of the actual data values. This is because when calling the UDF on a Snowpark DataFrame, you need to explicitly reference the columns using The correct way to apply the UDF to the DataFrame is to use the 'select' function with ' F.col()' to pass the column objects as arguments to the UDF.

NEW QUESTION # 238
You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?

A. Leveraging a distributed hyperparameter tuning framework like Ray Tune or Dask-ML within Snowpark Python, utilizing Snowflake's compute resources for parallel training and evaluation of different hyperparameter configurations.
B. Employing Scikit-learn's 'GridSearchCV' or 'RandomizedSearchCV' within a single Snowpark Python UDF, processing the entire dataset and hyperparameter search sequentially.
C. Implementing a custom hyperparameter optimization algorithm using Snowpark Python UDFs, manually distributing the hyperparameter search across multiple Snowflake virtual warehouses, and aggregating the results in a central table.
D. Using a simple 'for' loop to iterate through a predefined set of hyperparameter combinations, training the model for each combination within a Snowpark Python UDF, and storing the results in a Snowflake table.
E. Using 'Hyperopt' within a Snowpark Python UDF, relying on its sequential optimization approach without any form of parallelization or distribution of the workload.

Answer: A

Explanation:
Option B is the most scalable and performant solution. Distributed hyperparameter tuning frameworks like Ray Tune or Dask-ML are designed to efficiently parallelize the hyperparameter search process across multiple compute resources. By integrating these frameworks with Snowpark Python, you can leverage Snowflake's scalable compute infrastructure to train and evaluate multiple hyperparameter configurations simultaneously, significantly reducing the overall tuning time. Option A is inefficient as it relies on a serial process. Option C is limited by the computational resources of a single Snowpark Python UDF. Option D is complex and requires manual management of distributed tasks, making it less efficient and scalable than using a dedicated framework. Option E is also limited by its sequential nature and does not take advantage of Snowflake's distributed computing capabilities.

NEW QUESTION # 239
You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

A. Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.
B. Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
C. Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.
D. Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
E. Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.

Answer: A,D,E

Explanation:
Options B, C, and E are the correct strategies. Caching (B) reduces redundant calls. A queueing system (C) provides precise rate control but adds complexity. A retry mechanism with backoff (E) handles rate limit errors gracefully. Sleeping within a UDF (A) is inefficient and inaccurate, as it doesn't account for network latency or processing time. Scaling up the warehouse (D) might increase concurrency but won't directly address the per-second rate limit of the external API and could be cost-prohibitive.

NEW QUESTION # 240
You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:

A. Do not scale the data, as K-means is robust to differences in feature scales.
B. Apply MinMaxScaler to all three features to scale them to a range between O and 1 .
C. Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.
D. Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.
E. Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.

Answer: B,C,E

Explanation:
K-means clustering is sensitive to the scale of the features because it relies on distance calculations. Features with larger values will have a disproportionate influence on the clustering results. StandardScaler centers the data around zero and scales it to unit variance, which ensures that all features have a similar range and variance. MinMaxScaler scales the features to a range between O and 1, which also addresses the issue of different scales. RobustScaler handles outliers which will then use the other two scaling techniques. Therefore A, B and D are the appropriate scaling techniques. C is not correct as K-means relies on distance calculations and not scaling the data could give some feature a larger weight which isn't the desired outcome. Option E: Using PowerTransformer on 'income' to reduce skewness and StandardScaler on the other features can be a valid approach, but it depends on the distribution of 'income' and the presence of outliers. If 'income' is highly skewed and/or contains outliers, this combination might be more effective than using StandardScaler or MinMaxScaler alone.

NEW QUESTION # 241
You've created a Python stored procedure in Snowflake to train a model. The procedure successfully trains the model, saves it using 'joblib.dump' , and then attempts to upload the model file to an internal stage. However, the upload fails intermittently with a FileNotFoundErroN. The stage is correctly configured, and the stored procedure has the necessary privileges. Which of the following actions are MOST likely to resolve this issue? (Select TWO)

A. Before uploading the model to the stage, verify that the file exists using 'os.path.exists()' within the stored procedure. If the file does not exist, log an error and raise an exception.
B. Before uploading the model to the stage, explicitly create the directory within the stage using 'snowflake.connector.connect()' and executing a 'CREATE DIRECTORY IF NOT EXISTS command on the stage. Then retry upload.
C. Use the fully qualified path for the model file when calling 'joblib.dump'. E.g., 'joblib.dump(model, '/tmp/model.joblib')' instead of 'joblib.dump(model, 'model .joblib')'.
D. Implement error handling within the Python code to catch the 'FileNotFoundError' and retry the file upload after a short delay using 'time.sleep()'. The stored procedure should retry the upload a maximum of 3 times before failing.
E. Ensure that the Python packages used within the stored procedure (e.g., scikit-learn, joblib) are explicitly listed in the 'imports' clause of the 'CREATE PROCEDURE statement.

Answer: A,C

Explanation:
The ' FileNotFoundError' often occurs because the default working directory within the Snowflake Python execution environment is not what's expected, or the file isn't being saved where expected. Using a fully qualified path (Option B) ensures that the model is saved to a known location, typically '/tmpP. Verify if file exist (Option E) will ensure you have written model to a file and prevent exception before upload file to Stage. Options A is not relevant to the FileNotFoundError problem. Option C is just a workaround not a real solution. Option D makes no sense.

NEW QUESTION # 242
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