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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q216-Q221):

NEW QUESTION # 216
You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

A. Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.
B. Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.
C. Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.
D. Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.
E. Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.

Answer: E

Explanation:
Storing models in external stages with versioning allows you to easily manage different model versions. Snowflake metadata points to the correct version, and UDFs can load them. Time Travel is useful, but is not ideal for large binary files. Option A is possible, but leads to potentially large and unwieldy Snowflake tables. Option C is not recommended as manual processes can lead to human errors and overwriting active models directly without proper model management creates deployment risks. Deleting older models (option E) prevents rollback.

NEW QUESTION # 217
You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

Answer: C,D

Explanation:
Options C and E are correct. Both use a subquery to calculate the rank of each product within each month's sales, then filter for the top 5 products. The main difference is that option C uses DENSE_RANK(), which assigns consecutive ranks even if there are ties in sales amount (resulting in more than 5 products being selected if there are ties for the 5th position), while option E uses RANK(), which assigns the same rank to tied values but can skip ranks. Option A is incorrect because it attempts to filter using HAVING on a ranking calculated within the same query level, which is not allowed in many SQL implementations (and can be logically incorrect). Options B and D are incorrect as they employ ROW_NUMBER() and NTILE(5) respectively. ROW_NUMBER will not handle ties correctly, while NTILE just divides the data into 5 groups without explicitly identifying the 'top' 5. Option A uses a rank function inside the HAVING clause which is often syntactically invalid.

NEW QUESTION # 218
You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

A. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
B. Explicitly specify a in the call.
C. Create an associated Stream on the source tables used for Feature Groups
D. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
E. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.

Answer: A,E

Explanation:
Options B and C are correct. B: Specifying a 'timestamp_key' during Feature Group creation is crucial for enabling point-in-time correctness. This tells the Feature Store which column represents the event timestamp. C: The method is specifically designed for point-in-time lookups. It requires a dataframe containing primary keys and the desired timestamp for each lookup. This enables the Feature Store to retrieve the feature values as they were at that specific point in time. Option A is incorrect, while enabling CDC is valuable for incremental updates, it does not guarantee point-in-time correctness without specifying the timestamp key and retrieving historical features using that key. Option D is not necessary, streams enable incremental loads but are separate from point in time. Option E, is not needed, its implicit via using .

NEW QUESTION # 219
You have built an external function to train a PyTorch model using SageMaker. The model training process requires a significant amount of CPU and memory. The training data is passed from Snowflake to the external function in batches. The external function code in AWS Lambda is as follows:

The Snowflake external function is defined as follows:

During testing, you encounter '500 Internal Server Error' from the external function consistently. Upon inspection of the Lambda logs, you find messages indicating 'PayloadTooLargeError'. What is the most likely cause and how do you mitigate it within the context of Snowflake and AWS Lambda?

A. The Snowflake external function definition is incorrect. Change the 'RETURNS VARIANT clause to 'RETURNS VARCHAR as the Lambda function returns a JSON string.
B. The Lambda function is timing out before the model training can complete. Increase the Lambda function's timeout setting to allow sufficient time for the training process.
C. The IAM role associated with the Lambda function lacks the necessary permissions to invoke the SageMaker training job. Grant the Lambda function's IAM role the appropriate SageMaker permissions.
D. The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWSAPI Gateway. Increase the maximum payload size limit in the API Gateway settings.
E. The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWS API Gateway. Implement data partitioning in Snowflake and send smaller batches of data to the Lambda function, aggregating the results in a separate table.

Answer: E

Explanation:
The 'PayloadTooLargeError' indicates that the data being passed from Snowflake exceeds API Gateway's payload limit. Option D provides the correct solution: partitioning the data in Snowflake and sending smaller batches, which keeps individual payloads within the limits, and the training can still complete with all data through multiple calls. Option A is generally not recommended because increasing the payload size limit indefinitely can have negative impacts on API Gateway performance. Other reasons, such as Lambda timeout or SageMaker permissions could contribute, but the immediate cause is the payload size. Option C does not resolve this issue. Returns VARIANT is correct

NEW QUESTION # 220
A data science team is tasked with deploying a pre-built anomaly detection model in Snowflake to identify fraudulent transactions. They need to use Snowflake ML functions and a Snowflake Native App (that houses the model) to achieve this. The Snowflake Native App is installed and available. The transaction data is stored in a table called 'TRANSACTIONS. Which of the following steps are essential to successfully deploy and use this pre-built model within a User Defined Function (UDF) for real-time scoring, assuming the app provides a function named 'ANOMALY SCORE?

A. Train the pre-built anomaly detection model using Snowflake's ML functions (e.g., 'CREATE MODELS) with the 'TRANSACTIONS' data before creating the UDE
B. Create a UDF that calls the 'ANOMALY _ SCORE function provided by the Snowflake Native App, passing the relevant transaction features as arguments.
C. Create an external function in API Integration instead of UDF.
D. Grant the USAGE privilege on the Snowflake Native App to the role executing the UDF. This ensures the UDF can access the app's functionality.
E. Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.

Answer: B,D

Explanation:
Options A and B are correct. A UDF is required to call the function from the Native App and to expose the model's functionality for scoring. Granting 'USAGE on the app to the executing role is necessary for the UDF to access the app's functions. Option C is incorrect because UDFs pass data as arguments, avoiding the need to share tables directly. Option D is incorrect since you're using pre-built model, training isn't needed. Option E is incorrect since it specifically asks for UDF with a Snowflake Native App.

NEW QUESTION # 221
......

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