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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

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

Answer: A

Explanation:
Option C is the most efficient because it correctly calculates the number of days since the last transaction using and 'DATEDIFF. The 'OR REPLACE clause ensures that the materialized view can be updated if it already exists. Options A and B are syntactically identical but A is slightly more correct since it considers the MAX. Option D calculates recency from the first transaction, which is incorrect. Option E is similar to option C but less performant since we want datediff on max(transaction_date) and not calculate and take the max over it.

NEW QUESTION # 106
You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
B. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
C. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
D. Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
E. Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.

Answer: E

Explanation:
Option C is the most efficient. 'execute_stream' allows you to fetch data in chunks, preventing out-of-memory errors with large tables. You can perform transformations on each chunk, reducing the memory footprint. Loading the entire table at once (A) is inefficient for large datasets. Using ssnowsqr (B) or 'COPY INTO' (E) adds an extra step of unloading and reloading, increasing the time taken. Creating a Snowflake view (D) is a good approach for pre-processing but might not fully address memory issues during the final load into Pandas, especially if the view still contains a large amount of data.

NEW QUESTION # 107
You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

A. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.
B. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.
C. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
D. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.
E. Use the role within the pipeline script to create and grant all necessary privileges.

Answer: D

Explanation:
The principle of least privilege dictates that the pipeline should only have the minimum necessary privileges to perform its tasks. Creating a custom role with only the required privileges and granting it to a dedicated service account is the most secure approach. Using 'ACCOUNTADMIN' (Option A) or 'SYSADMIN' (Option C) grants excessive privileges. Hardcoding credentials (Option D) is a major security vulnerability. Granting 'OWNERSHIP (Option E) is generally not necessary and grants excessive control. This follows the principle of least privilege which is essential for secure Snowflake deployments. A dedicated role ensures that the pipeline cannot inadvertently perform actions outside of its intended scope.

NEW QUESTION # 108
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. Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.
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. Train the pre-built anomaly detection model using Snowflake's ML functions (e.g., 'CREATE MODELS) with the 'TRANSACTIONS' data before creating the UDE
E. 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.

Answer: B,E

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 # 109
You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

A. Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
B. Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.
C. Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
D. Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
E. Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.

Answer: C,D

Explanation:
Options A and C are correct. Increasing the warehouse size provides more compute resources, leading to faster execution. Vectorized UDFs (especially Pandas UDFs for Python-based models) are highly efficient for batch processing, as they leverage Snowflake's parallel processing capabilities. B is incorrect as Snowpark UDFs are often more efficient due to their ability to use compiled languages and optimized libraries. Result caching (Option D) might help if the same input data is frequently used, but it won't improve the performance for new data. Row-level security (Option E) is primarily for security and won't directly improve UDF performance in this context.

NEW QUESTION # 110
......

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DSA-C03 Valid Exam Voucher, Pass4sure DSA-C03 Dumps Pdf

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