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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
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 # 78
You are tasked with deploying a real-time fraud detection model in Snowflake. The model requires very low latency (under 100ms) to prevent fraudulent transactions. The input data is streamed into a Snowflake table. You are considering using either a Scalar or Vectorized Python UDF for scoring. Which of the following approaches and considerations are MOST critical for achieving the desired performance and reliability? Assume the model itself is computationally inexpensive. Select all that apply.

A. Pre-load the model into a static variable within the UDF code, ensuring it's only loaded once per worker node.
B. Use a Vectorized UDF with a small 'MAX BATCH_SIZE to minimize latency while still leveraging vectorization benefits.
C. Utilize Snowflake's Materialized Views to pre-compute frequently used features, reducing the amount of data the UDF needs to process.
D. Configure Snowflake's Auto-Suspend feature to aggressively suspend the warehouse when idle, to minimize costs.
E. Use a Scalar UDF because it has lower overhead per invocation compared to a Vectorized UDF when processing individual transactions.

Answer: A,B,C

Explanation:
For real-time fraud detection with low latency requirements, careful optimization is crucial. Vectorized UDFs (B) can be faster than scalar UDFs even with small batch sizes because of the reduced overhead per record compared to scalar UDFs. Pre-loading the model (C) is essential to avoid repeated model loading overhead. Using Materialized Views (D) to pre-compute features reduces the data the UDF needs to handle, improving performance. While scalar UDFs can have lower overhead per invocation, vectorized UDFs optimized with proper will generally provide better performance. Aggressively auto-suspending the warehouse (E) is counterproductive as it introduces latency due to warehouse startup time.

NEW QUESTION # 79
You are analyzing website traffic data stored in a Snowflake table named 'WEB EVENTS. This table contains a 'TIMESTAMP' column representing when the event occurred and a 'PAGE VIEWS column indicating the number of page views for that event. You need to identify the day with the highest number of page views and also the day with lowest number of page views along with average number of page views. How can you accomplish this using Snowflake SQL?

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

Answer: D

Explanation:
Option D provides the correct answer. The first two queries correctly identify the day with the highest and lowest total views, respectively, using 'DATE(TIMESTAMP)' to extract the date, to aggregate page views, 'GROUP BY' to group by date, "ORDER BY' to sort, and 'LIMIT 1' to select only the top/bottom day. It also has the correct query to identify average page views 'SELECT OVER() FROM WEB_EVENTS LIMIT Other Options A and E are quite close but they don't identify the same, in option A, 'SELECT AVG(PAGE_VIEWS) FROM WEB_EVENTS' , the AVG page views won't tell us the dates of min max and Avg views. Similar is the problem with option E, 'SELECT FROM WEB_EVENTS The APPROX_AVG won't tell us which day has highest or lowest.

NEW QUESTION # 80
You have successfully deployed a machine learning model in Snowflake using Snowpark and are generating predictions. You need to implement a robust error handling mechanism to ensure that if the model encounters an issue during prediction (e.g., missing feature, invalid data type), the process doesn't halt and the errors are logged appropriately. You are using a User-Defined Function (UDF) to call the model. Which of the following strategies, when used IN COMBINATION, provides the BEST error handling and monitoring capabilities in this scenario?

A. Wrap the prediction call in a 'SYSTEM$QUERY_PROFILE function to get detailed query execution statistics and identify potential performance bottlenecks.
B. Use Snowflake's event tables to capture errors and audit logs related to the UDF execution.
C. Implement a custom logging solution by writing error messages to an external file storage (e.g., AWS S3) using an external function called from within the UDE
D. Rely solely on Snowflake's query history to identify failed predictions and debug the model, without any explicit error handling within the UDE
E. Use a 'TRY...CATCH' block within the UDF to catch exceptions, log the errors to a separate Snowflake table, and return a default prediction value (e.g., NULL) for the affected row.

Answer: B,E

Explanation:
The combination of A and D provides the best error handling and monitoring. A 'TRY...CATCH' block within the UDF allows for graceful handling of exceptions and prevents the entire process from failing. Logging errors to a separate Snowflake table allows for easy analysis and debugging. Returning a default value ensures that downstream applications don't encounter unexpected errors due to missing predictions. Snowflake's event tables capture a broader range of errors and audit logs, providing a comprehensive view of the UDF's execution. Option B is insufficient as it relies solely on post-mortem analysis. Option C is useful for performance profiling but doesn't address error handling directly. Option E introduces external dependencies and complexity when a native Snowflake solution is available and potentially introduces latency in the prediction process. It also can impact costs since you are using external function to copy the logs outside snowflake, where cost will be charged.

NEW QUESTION # 81
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. Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.
B. AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.
C. Recall, optimized with a threshold adjustment to minimize false negatives.
D. F 1-Score, optimized to balance precision and recall equally.
E. Precision, optimized with a threshold adjustment to minimize false positives.

Answer: E

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 # 82
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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q77-Q82):

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