Free PDF Perfect Snowflake - DSA-C03 Valid Test Topics

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
You are a data scientist working for a retail company. You've been tasked with identifying fraudulent transactions. You have a Snowflake table named 'TRANSACTIONS' with columns 'TRANSACTION ID', 'AMOUNT', 'TRANSACTION DATE', 'CUSTOMER ID', and 'LOCATION'. You suspect outliers in transaction amounts might indicate fraud. Which of the following SQL queries is the MOST efficient and appropriate to identify potential outliers using the Interquartile Range (IQR) method, and incorporate necessary data type considerations for robust percentile calculations? Consider also the computational cost associated with each approach on a large dataset.

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

Answer: B

Explanation:
Option B is the most efficient and readable. It calculates the IQR values (QI and Q3) once in a CTE (Common Table Expression) called IQR_Values' and then uses these values to filter the 'TRANSACTIONS table. The 'APPROX_PERCENTILE function is used for efficient approximation on large datasets. Using QUALIFY (option C) is syntactically valid but it can be less performant than using a CTE in this scenario, especially if the data requires significant scanning across multiple partitions or micro-partitions due to the window function. Option A, C and D are inefficient because they calculate the percentiles multiple times. Option E uses a JOIN, which although can be functionally correct, might be less clear than filtering within the CTE-based approach.

NEW QUESTION # 186
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 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: E

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 # 187
You are tasked with identifying Personally Identifiable Information (PII) within a Snowflake table named 'customer data'. This table contains various columns, some of which may contain sensitive information like email addresses and phone numbers. You want to use Snowflake's data governance features to tag these columns appropriately. Which of the following approaches is the MOST effective and secure way to automatically identify and tag potential PII columns with the 'PII CLASSIFIED tag in your Snowflake environment, ensuring minimal manual intervention and optimal accuracy?

A. Use Snowflake's built-in classification feature with a pre-defined sensitivity category to identify potential PII columns. Associate a masking policy that redacts the data, and apply a tag 'PII_CLASSIFIED' via automated tagging to the columns identified as containing PII.
B. Create a custom Snowpark for Python UDF that uses regular expressions to analyze the data in each column and apply the 'PII_CLASSIFIED tag if a match is found. Schedule this UDF to run periodically using Snowflake Tasks.
C. Write a SQL script to query the 'INFORMATION SCHEMA.COLUMNS' view, identify columns with names containing keywords like 'email' or 'phone', and then apply the 'PII_CLASSIFIED tag to those columns.
D. Manually inspect each column in the 'customer_data' table and apply the 'PII_CLASSIFIED' tag to columns that appear to contain PII based on their names and a small sample of data.
E. Export the 'customer_data' to a staging area in cloud storage, use a third-party data discovery tool to scan for PII, and then manually apply the "PII_CLASSIFIED' tag to the corresponding columns in Snowflake based on the tool's findings.

Answer: A

Explanation:
Snowflake's built-in classification feature is the most effective because it uses machine learning models to automatically identify sensitive data with a high degree of accuracy. Associating masking policies with the identified columns provides additional data protection. Automated tagging further streamlines the governance process. Option A, while viable, requires custom code and maintenance. Option C is manual and error-prone. Option D is based solely on column names and can lead to false positives and negatives. Option E introduces unnecessary complexity and security risks by exporting data.

NEW QUESTION # 188
You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A. Precision, optimized with a threshold adjustment to minimize false positives.
B. Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
C. AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.
D. F 1-Score, optimized to balance precision and recall equally.
E. Recall, optimized with a threshold adjustment to minimize false negatives.

Answer: A

Explanation:
Precision is the most suitable optimization metric because it focuses on minimizing false positives. In fraud detection, incorrectly flagging legitimate transactions as fraudulent can have significant negative consequences for customers and the business. By optimizing for precision and adjusting the prediction threshold to further minimize false positives, you can ensure that the model identifies fraudulent transactions with a high degree of certainty. Recall would prioritize catching all fraud cases, even at the cost of increased false positives, which is not desirable in this scenario. While F1 balances precision and recall, the scenario specifically prioritizes precision. AUC-ROC is a good general measure of performance but does not directly address the specific requirement of minimizing false positives.

NEW QUESTION # 189
Which of the following statements about Z-tests and T-tests are generally true? Select all that apply.

A. A T-test is generally used when the sample size is large (n > 30) and the population standard deviation is known.
B. A T-test has fewer degrees of freedom compared to the Z-test, making it more robust to outliers.
C. A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data.
D. Both Z-tests and T-tests assume that the data is non-normally distributed.
E. As the sample size increases, the T-distribution approaches the standard normal (Z) distribution.

Answer: C,E

Explanation:
The correct answers are A and C. A Z-test requires knowing the population standard deviation, while a T-test estimates it from the sample data. As the sample size increases, the T-distribution approaches the standard normal (Z) distribution, which is a core concept in statistical inference. B is incorrect because a T-test is generally used for small sample sizes (n < 30) or when the population standard deviation is unknown. D is incorrect because both tests assume the underlying population distribution is approximately normal, especially for smaller sample sizes (though the Central Limit Theorem allows us to relax this assumption somewhat for large samples). E is incorrect because fewer degrees of freedom make the t-test less robust to outliers. Also the robustness is provided by the population distribution being approximately normal.

NEW QUESTION # 190
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Free PDF Perfect Snowflake - DSA-C03 Valid Test Topics

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q185-Q190):

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