HELP YOU LEARN STEPS NECESSARY TO PASS THE DSA-C03 EXAM EXAM COURSE

Help You Learn Steps Necessary To Pass The DSA-C03 Exam Exam Course

Help You Learn Steps Necessary To Pass The DSA-C03 Exam Exam Course

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

Answer: C

Explanation:
Option C is the most secure and efficient. It correctly uses the 'API_INTEGRATION' to leverage Snowflake's security features for managing credentials. It also includes the 'HEADERS' parameter to specify the content type, which is essential for proper communication with the SageMaker endpoint. REQUEST and RESPONSE translators can avoid some of the boilerplate JSON building and parsing within the Snowflake environment. The other options are either missing crucial headers or do not use the API integration securely.


NEW QUESTION # 47
You have trained a complex Random Forest model in Snowflake to predict loan default risk. You wish to understand the individual and combined effects of 'credit_score' and 'debt_to_income_ratio' on the predicted probability of default. Which approach is MOST suitable for visualizing and interpreting these relationships?

  • A. Create a two-way Partial Dependence Plot (PDP) showing the interaction between 'credit_score' and 'debt_to_income_ratio'.
  • B. Examine the model's overall accuracy (e.g., AUC) and assume the relationships are well-represented.
  • C. Calculate feature importance using SNOWFLAKE.ML.FEATURE IMPORTANCE and focus on the features with the highest scores.
  • D. Fit a simpler linear model (e.g., Logistic Regression) to the data and interpret its coefficients.
  • E. Generate individual Partial Dependence Plots (PDPs) for 'credit_score' and 'debt_to_income_ratio'.

Answer: A

Explanation:
The correct answer is C. While individual PDPs (option B) provide insights into the individual effects of each feature, a two-way PDP specifically visualizes and helps interpret the interaction between 'credit_score' and 'debt_to_income_ratio'. This is crucial for understanding how the combined effect of these features influences the predicted probability of default. Feature importance (option A) indicates feature relevance but doesn't show the nature of the relationship. Simplifying the model (option D) sacrifices the complexity captured by the Random Forest. Overall accuracy (option E) doesn't provide specific insights into feature relationships.


NEW QUESTION # 48
You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

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

Answer: C

Explanation:
Creating a VIEW (option C) is the BEST approach for several reasons. It doesn't modify the underlying data, which is crucial for data govemance and prevents unintended side effects. The feature is calculated on-the-fly whenever the view is queried, ensuring that the feature is always up-to-date if the underlying changes. Options A, D, and E permanently alter the table, potentially leading to data redundancy and requiring manual updates if the column changes. Option B creates a temporary table, which is suitable for short-lived experiments but not ideal for a feature that will be used repeatedly. Using 2) is equivalent to CUSTOMER_AGE CUSTOMER_AGE. Views are efficient because Snowflake's query optimizer can often push down computations into the underlying table. Option C also avoids needing to manage the lifecycle of updated calculated columns.


NEW QUESTION # 49
You're working on a fraud detection system for an e-commerce platform. You have a table 'TRANSACTIONS with a 'TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories ('Low', 'Medium', 'High', 'Very High') using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the 'WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?

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

Answer: A

Explanation:
Option E correctly uses with an array of bin boundaries (0, 100, 500, 1000). 'WIDTH_BUCKET returns the bucket number the value falls into. The CASE statement then assigns labels based on the bucket number. Other options either do not correctly use 'WIDTH_BUCKET with an array, use hardcoded values, or do not handle the 'Very High' category properly. Note that WIDTH BUCKET(value, array) is a Snowflake extension and is the preferred, and potentially most efficient, method for binning into distinct intervals with explicit boundaries. Option C is incorrect as it doesn't implement width_bucket function. Option A is correct as it handles very high transactions by including a maximum value , but the width is equal for all buckets.


NEW QUESTION # 50
You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

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

Answer: C

Explanation:
Option C is the most effective for this scenario. Focusing on sessions and their durations provides a more meaningful feature for predicting conversion rates. Removing bot traffic (A) might be a useful preprocessing step but doesn't fundamentally address session-level relevance. Option B's logic is flawed removing all Internet Explorer traffic isn't inherently removing irrelevant data. Option D oversimplifies the data, losing valuable information about user behavior within sessions. Option E introduces bias by randomly sampling and removing potentially important patterns, plus it is too simplistic. The code example in C demonstrates how to calculate session duration using Snowpark functions, join the filtered session data back to the original data, and then drop the irrelevant columns.


NEW QUESTION # 51
......

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