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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q250-Q255):

NEW QUESTION # 250
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. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
B. 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.
C. 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.
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: C,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 # 251
A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

A. The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
B. The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
C. Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
D. The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.
E. Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.

Answer: B,D

Explanation:
Options B and C are correct. Bootstrapping relies on the assumption that the original sample is representative of the population. If it isn't, the bootstrap distribution will reflect the biases of the sample. Also certain statistics, particularly those sensitive to outliers or with high variance, can produce bootstrap distributions that differ significantly from the original data distribution. Option A is incorrect because the bootstrap distribution doesn't necessarily have to be same as sample distribution. Option D is incorrect since Bootstrapping makes no assumptions regarding the distribution of original dataset and can be used for any data distribution. Option E is not correct. Bootstrapping is not always accurate and relies on assumptions to perform correctly.

NEW QUESTION # 252
You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

Answer: A

Explanation:
Option E is the best approach. It uses to calculate the mean, to calculate the median (robust to outliers), to calculate the standard deviation (measure of spread), and 'QUANTILE(transaction_amount, 0.25, 0.5, 0.75)' to calculate the quartiles (25th, 50th, and 75th percentiles), all grouped by 'customer_segment'. This provides a comprehensive view of the distribution. Option A only provides an approximate count of distinct transaction amounts and the average. Option B provides standard deviation, variance, and median but lacks the mean and quartiles. Option C provides the range and count, which are useful but not as comprehensive. Option D calculates correlation and covariance, which are useful for understanding the relationship between transaction amount and customer segment (assuming customer segment is appropriately encoded numerically), but not for analyzing the distribution within each segment. It is important to note that 'QUANTILE' can also be accomplished using 'APPROX_PERCENTILE'

NEW QUESTION # 253
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

A. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
B. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
C. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
E. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.

Answer: D

Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.

NEW QUESTION # 254
You've deployed a fraud detection model in Snowflake. The model is implemented as a Python UDF that uses a pre-trained scikit-learn model stored as a stage file. Your goal is to enable near real-time fraud detection on incoming transactions. Due to regulatory requirements, you need to maintain a detailed audit trail of all predictions, including the input features, model version, prediction scores, and any errors encountered during the prediction process. Which of the following approaches are valid and efficient for storing these audit logs and predictions in Snowflake?

A. Use Snowflake's 'SYSTEM$QUERY LOG' table to extract information about the UDF execution and join it with the transaction data to reconstruct the audit trail.
B. Store the audit logs as unstructured text files in an external stage (e.g., AWS S3) and periodically load them into a Snowflake table using COPY INTO command.
C. Log the audit information to an external logging service (e.g., Splunk) using an external function called from within the UDF.
D. Utilize Snowflake's Streams and Tasks to automatically capture changes to the transaction table and trigger the prediction UDF, storing the audit logs in a separate table with similar structure as described in option A.
E. Create a dedicated table with columns for transaction ID, input features (as a JSON VARIANT), model version, prediction score, error message (if any), and prediction timestamp. Use a Snowflake Sequence to generate unique log IDs.

Answer: D,E

Explanation:
Options A and C are the most valid and efficient approaches. Option A provides a structured and readily queryable format for the audit logs, making it easy to analyze and report on fraud detection performance. Using a SEQUENCE ensures unique and ordered log IDs. Option C leverages Snowflake's Streams and Tasks to automate the prediction process and audit logging, ensuring that all transactions are processed and logged in near real-time. This is particularly suitable for continuous fraud detection. Option B is less efficient due to the overhead of loading unstructured data and parsing it. It lacks real-time processing capabilities. Option D introduces external dependencies and potential latency. While external logging services can be valuable, storing the audit data natively in Snowflake provides better integration and performance. Option E is not reliable for recreating the full audit trail, as primarily captures query execution metadata and may not contain all the necessary information (e.g., input features, model version). Also SYSTEM$QUERY LOG data availability can be delayed.

NEW QUESTION # 255
......

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DSA-C03 Test Study Guide - Vce DSA-C03 Test Simulator

Vce Snowflake DSA-C03 Test Simulator, DSA-C03 Valid Practice Materials

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q250-Q255):

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