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[General] Top Snowflake DSA-C03 Dumps, DSA-C03 Exam Simulations

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【General】 Top Snowflake DSA-C03 Dumps, DSA-C03 Exam Simulations

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q186-Q191):NEW QUESTION # 186
You are building a predictive model on customer churn using Snowflake data'. You observe that the distribution of 'TIME SINCE LAST PURCHASE' is heavily left-skewed. Which of the following strategies would be MOST appropriate to handle this skewness before feeding the data into a linear regression model to improve its performance? (Select TWO)
  • A. Remove all records with 'TIME SINCE LAST PURCHASE' values below the mean.
  • B. Standardize the 'TIME_SINCE_LAST_PURCHASE' column using Z-score normalization.
  • C. Apply a logarithmic transformation to the 'TIME SINCE LAST PURCHASE' column.
  • D. Apply a square root transformation to the 'TIME_SINCE_LAST_PURCHASE' column.
  • E. Use a winsorization technique to cap extreme values in the 'TIME SINCE LAST PURCHASE' column at a predefined percentile (e.g., 99th percentile).
Answer: D,E
Explanation:
For left-skewed data, a square root transformation (Option B) can help reduce the impact of smaller values and bring the distribution closer to normal. Winsorization (Option C) can mitigate the influence of extreme values on the left tail of the distribution, making the data more suitable for linear regression. Logarithmic transformation is more suitable for right-skewed data (Option A). Z-score normalization (Option D) centers the data around zero but doesn't change the skewness. Removing records below the mean (Option E) is generally not a good practice as it can introduce bias and lose valuable information.

NEW QUESTION # 187
You have a dataset in Snowflake containing customer reviews. One of the columns, 'review_text', contains free-text customer feedback. You want to perform sentiment analysis on these reviews and include the sentiment score as a feature in your machine learning model. Furthermore, you wish to categorize the sentiment into 'Positive', 'Negative', and 'Neutral'. Given the need for scalability and efficiency within Snowflake, which methods could be employed?
  • A. Create a Snowpark Python DataFrame from the Snowflake table, use a sentiment analysis library within the Snowpark environment, categorize the sentiments, and then save the resulting DataFrame back to Snowflake as a new table.
  • B. Create a series of Snowflake SQL queries utilizing complex string matching and keyword analysis to determine sentiment based on predefined lexicons. Categories are assigned through CASE statements.
  • C. Use a Python UDF (User-Defined Function) with a pre-trained sentiment analysis library (e.g., NLTK or spaCy) to calculate the sentiment score and categorize it. Deploy the UDF in Snowflake and apply it to the 'review_text' column.
  • D. Utilize Snowflake's external functions to call a pre-existing sentiment analysis API (e.g., Google Cloud Natural Language API or AWS Comprehend) passing the review text and storing the returned sentiment score and category. Ensure proper API key management and network configuration.
  • E. Use a Snowflake procedure that reads all 'review_text' data, transfers data outside of Snowflake to an external server running sentiment analysis software, and then writes results back into a new table.
Answer: A,C,D
Explanation:
Options A, B, and C are viable and efficient methods for sentiment analysis within Snowflake. A Python UDF leverages the compute power of Snowflake while utilizing popular Python NLP libraries. Snowpark offers a scalable way to process data within Snowflake using Python. Snowflake's External Functions provide access to pre-built sentiment analysis APIs, which can be highly accurate but may incur costs based on API usage. Option D is not appropriate as it transfers the data out of Snowflake to perform the sentiment analysis, which is a bad design. Option E can be used as well but sentiment scores based on SQL are not going to be as accurate as calling an API or leveraging an established library.

NEW QUESTION # 188
You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)
  • A. Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.
  • B. Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.
  • C. Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
  • D. Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
  • E. Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.
Answer: A,B,C
Explanation:
Options A, B, and E are valid and efficient approaches. Option A using 'NTILE' is a direct and efficient way to create quantile bins within Snowflake SQL, and can find the most recent order date for customer with a case statement. Option B calculates the percentiles directly and then uses a CASE statement to assign bins. This is also efficient for explicit boundaries. Option E finds the boundaries of the quantile using 'APPROX_PERCENTILE or 'PERCENTILE_CONT , after that you can use 'WIDTH_BUCKET to categorize into quantile bins based on ranges. Option C is possible but generally less efficient due to the overhead of UDF execution and data transfer between Snowflake and the UDF environment. Option D is valid, but creating a temporary table adds complexity and potentially reduces performance compared to window functions or direct quantile calculation within the query.

NEW QUESTION # 189
You've deployed a regression model in Snowflake to predict product sales. After a month, you observe that the RMSE on your validation dataset has increased significantly compared to the initial deployment. Analyzing the prediction errors, you notice a pattern: the model consistently underestimates sales for products with a recent surge in social media mentions. Which of the following actions would be MOST effective in addressing this issue and improving the model's RMSE?
  • A. Incorporate a feature representing the number of social media mentions for each product into the model and retrain.
  • B. Increase the regularization strength of the model to prevent overfitting to the original training data.
  • C. Decrease the learning rate of the optimization algorithm during retraining to avoid overshooting the optimal weights.
  • D. Retrain the model using only the most recent data (e.g., last week) to adapt to the changing sales patterns.
  • E. Implement a moving average smoothing technique on the target variable (sales) before retraining the model.
Answer: A
Explanation:
Incorporating the social media mentions feature directly addresses the observed pattern in the errors. While other options might have some impact, adding the missing information is the most targeted and effective approach. Option A might help prevent overfitting, but doesn't address the missing information. Option B could lead to instability if the recent data isn't representative. Option D affects training but isn't specific to the issue. Option E smooths the target but doesn't explicitly account for social media influence.

NEW QUESTION # 190
You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?
  • A. Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.
  • B. Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
  • C. Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.
  • D. Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
  • E. Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
Answer: A,B,C,E
Explanation:
Options A, B, C and E are correct because: A: Correctly identifies the benefits (segment-specific models) and drawbacks (overfitting on small segments) of partitioning by 'customer_segment. B: Accurately describes the advantages (temporal patterns, walk-forward validation) and limitations (independence from signup date) of partitioning by 'signup_date' . C: Properly explains the use case (geographic influence), performance benefits (filtering), and potential drawbacks (data silos) of partitioning by 'region'. E: Correctly highlights the implementation overhead and potential skew issues associated with partitioning. Option D is incorrect because Clustering on top of paritioning is not always guranteed performance improvements without assessing underlying query patterns. Snowflake automatically partitions data into micro-partitions, so additional clustering might not always result in significant performance improvements.

NEW QUESTION # 191
......
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