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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

Answer: D

Explanation:
Option D is the most efficient and accurate because it uses a single SQL query to calculate the MSE directly. It avoids using cursors or procedural logic, which are less performant in Snowflake. It uses SUM to calculate the sum of squared errors and COUNT( ) to get the total number of records, then divides to obtain the average (MSE). Option B calculates the average of power, that is wrong mathematical operation, Option A is correct from mathematical point but slow because of cursor and not following Snowflake best practices, option C is using JavaScript which is also valid, but Snowflake recommends to use SQL when possible for performance, and option E is using external python for model calculation, that not best for this scenarios.

NEW QUESTION # 118
You are building a data science pipeline in Snowflake to predict customer churn. The pipeline includes a Python UDF that uses a pre- trained scikit-learn model stored as a binary file in a Snowflake stage. The UDF needs to load this model for prediction. You've encountered an issue where the UDF intermittently fails, seemingly related to resource limits when multiple concurrent queries invoke the UDF. Which of the following strategies would best optimize the UDF for concurrency and resource efficiency, minimizing the risk of failure?

A. Load the scikit-learn model outside the UDF function in the global scope of the module so that all invocations share the same loaded model instance. Use the 'context.getExecutionContext(Y to track execution, making sure it is thread safe.
B. Increase the memory allocated to the Snowflake warehouse to accommodate multiple UDF invocations.
C. Implement a global, lazy-loaded cache for the scikit-learn model within the UDF's module. The model is loaded only once during the first invocation and shared across subsequent calls. Protect the loading process with a lock to prevent race conditions in concurrent environments.
D. Utilize Snowflake's session-level caching by storing the loaded model in 'session.get('model')' to be reused across multiple UDF calls within the same session. Reload the model if 'session.get('model')' is None.
E. Load the scikit-learn model inside the UDF function on every invocation to ensure the latest version is used.

Answer: C

Explanation:
Option D provides the most efficient and robust solution. Loading the model only once (lazy loading) reduces overhead. A global cache ensures reusability. A lock is crucial to prevent race conditions during the initial loading in a concurrent environment. Option A is inefficient due to repeated loading. Option B is problematic because Snowflake UDFs do not directly support global variables in a thread-safe manner. Option C is incorrect as 'session.get' is not a valid Snowflake API for Python UDFs and lacks thread safety. Option E, while potentially helpful, doesn't address the underlying inefficiency of repeatedly loading the model.

NEW QUESTION # 119
A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

A. Calculated as 'applicant_age / applicant_income'.
B. Calculated as 'loan_amount I applicant_age' .
C. Calculated as 'applicant_income I loan_amount'.
D. Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
E. Calculated as 'applicant_age applicant_age'.

Answer: B,C,D

Explanation:
The best combination provides diverse perspectives on financial stability. directly reflects the applicant's ability to cover the loan with their income. represents the loan burden relative to the applicant's age and can expose risk in younger, less established applicants. provides the most comprehensive view, including existing debt obligations from external data. "age_squared' and are less directly informative about repayment ability. They could potentially capture non-linear relationships, but 'age_squareff is more likely to introduce overfitting. relies on an external data source, making it a powerful, but potentially more complex, feature to implement.

NEW QUESTION # 120
You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

Answer: E

Explanation:
Option B, using is the most efficient. The method directly retrieves the data as a Pandas DataFrame, leveraging Snowflake's internal optimizations for transferring data to Pandas. It's significantly faster than fetching rows individually or all at once and then creating the DataFrame. Also, it only selects the needed Columns. Option A fetches all columns and then tries to build dataframe from the list which is less effective. Option C would require additional setup with sqlalchemy and may introduce extra dependencies. Option D is also correct, but option B utilizes snowflake's internal optimizations for pandas retrieval making it best choice. Option E is also not effective as it only fetches 1000 records.

NEW QUESTION # 121
You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

A. Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
B. Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.
C. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
D. Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
E. Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.

Answer: B,D

Explanation:
Options A and E provide the most robust and scalable solutions. A UDF offers flexibility and reusability for data cleaning within Snowpark (Option A). Option E leverages Snowflake's data loading capabilities to clean data during ingestion and adds a UDF for cleaning existing data providing a comprehensive approach. Using a UDF written in Python and used within Snowpark leverages the power of Python's regular expression capabilities and the distributed processing of Snowpark. Handling data transformations during ingestion with Snowflake's built- in COPY INTO with transformation is highly efficient. Option B is less scalable and maintainable for complex formatting. Option C is viable but executing SQL stored procedures from Snowpark Python loses some of the advantages of Snowpark. Option D addresses data masking not data transformation.

NEW QUESTION # 122
......

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New Exam DSA-C03 Braindumps | DSA-C03 100% Accuracy

DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam Perfect New Exam Braindumps

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q117-Q122):

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