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Valid Dumps Snowflake DSA-C03 Sheet | Valid Test DSA-C03 Fee

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
Consider you are working on a credit risk scoring model using Snowflake. You have a table 'credit data' with the following schema: 'customer id', 'age', 'income', 'credit_score', 'loan_amount', 'loan_duration', 'defaulted'. You want to create several new features using Snowflake SQL to improve your model. Which combination of the following SQL statements will successfully create features for age groups, income-to-loan ratio, and interaction between credit score and loan amount using SQL in Snowflake? Choose all that apply.

Answer: B,D

Explanation:
Options D and E are correct. Option D creates a VIEW that dynamically calculates all three features without modifying the underlying table. A view is the correct and recommended usage. Option E creates a new table with all the features including the new engineered features. Option A creates a column and updates it, but this is inefficient compared to creating the feature directly in a single SELECT statement (Option E). B createa a temporary table but does not contain all three features. Option C, it only addresses the interaction feature, not age_group or income to loan ratio.

NEW QUESTION # 29
You have trained a machine learning model in Snowflake using Snowpark Python to predict customer churn. You want to deploy this model as a Snowflake User-Defined Function (UDF) for real-time scoring of new customer data arriving in a stream. The model uses several external Python libraries not available by default in the Anaconda channel. Which sequence of steps is the MOST efficient and correct way to deploy the model within Snowflake to ensure all dependencies are met?

A. Create a virtual environment locally with all required dependencies installed. Package the entire virtual environment into a zip file. Upload the zip file to a Snowflake stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the zip file in the 'imports' parameter. Snowflake will automatically extract the zip and use the virtual environment during UDF execution.
B. Create a Snowflake stage, upload the model file and all dependency .py' files. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the 'imports parameter with all the file names. Snowflake will interpret all .py' files as module for UDF execution.
C. Create a Snowflake stage and upload the model file. Create a conda environment file ('environment.yml') specifying the dependencies. Upload the environment.yml file to the stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and the environment.yml file in the 'imports' and 'packages' parameters, respectively. Snowflake will create a conda environment based on the environment.yml file during UDF execution.
D. Package the model file and all dependencies into a single Python wheel file. Upload this wheel file to a Snowflake stage. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the wheel file in the 'imports' parameter. Snowflake will automatically install the wheel during UDF execution.
E. Create a Snowflake stage, upload the model file and a 'requirements.txt' file listing the dependencies. Create the UDF using 'CREATE OR REPLACE FUNCTION' statement, referencing the stage and specifying the 'imports' parameter with the model file and requirements.txt. Snowflake will automatically install the dependencies from the 'requirements.txt' file during UDF execution.

Answer: D

Explanation:
Packaging the model and its dependencies into a single Python wheel file is the recommended and most efficient approach. Uploading the wheel to a stage and referencing it in the 'imports' parameter allows Snowflake to handle dependency resolution seamlessly. Options A and C assume Snowflake can directly install dependencies from a requirements.txt or environment.yml file, which is not directly supported. Option D is unnecessarily complex as it involves packaging an entire virtual environment. Option E will not handle complex external packages.

NEW QUESTION # 30
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. 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.
B. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
C. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
D. 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.
E. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.

Answer: D

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 # 31
You are building a model to predict loan defaults using a dataset stored in Snowflake. After training your model and calculating residuals, you create a scatter plot of the residuals against the predicted values. The plot shows a cone-shaped pattern, with residuals spreading out more as the predicted values increase. Which of the following SQL queries, run within a Snowpark Python session, could be used to address the underlying issue indicated by this residual pattern, assuming the predicted values are stored in a column named and the residuals in a column named 'loan_default_residuar in a Snowflake table named 'loan_predictionds'?

Answer: A

Explanation:
A cone-shaped pattern in the residuals plot (heteroscedasticity) indicates that the variance of the errors is not constant. Applying a transformation like Box-Cox to the target variable before retraining the model (Option D) is the most appropriate way to address this. Option A attempts to filter outliers based on the residuals, but does not address the heteroscedasticity itself and requires statistical functions unavailable within standard SQL. Option B attempts to take the natural log of the residuals, which is nonsensical as residuals can be negative. Option C attempts to filter based on the rank of residuals, which is similarly unhelpful, does not fix the problem, and uses inappropriate outlier removal with SQL QUALIFY clause. Option E scaling the features might sometimes improve model performance, but it does not directly address heteroscedasticity.

NEW QUESTION # 32
You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?

A.
B.
C. None of the above
D.
E.

Answer: A

Explanation:
Option A correctly addresses all requirements: Filters usage data for the last 30 days. Corrects negative values by setting them to 0 using and ' Calculates the sum of for each customer. Uses a 'LEFT JOIN' to include all customers, even those without recent usage data. Uses 'coalesce()' to set the to 0 for customers with no usage data after the join. Option B uses an ' INNER JOIN' , which would exclude customers without any recent usage data, violating the requirement to include all customers. Option C does not treat negative usage values correctly. Option D uses a "RIGHT JOIN' which would return incorrect results. Option E isn't right as option A correctly addresses all the scenarios.

NEW QUESTION # 33
......

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