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[General] High DSA-C03 Passing Score - DSA-C03 Valid Exam Preparation

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【General】 High DSA-C03 Passing Score - DSA-C03 Valid Exam Preparation

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q128-Q133):NEW QUESTION # 128
You are a data scientist working with a Snowflake table named 'CUSTOMER TRANSACTIONS' that contains sensitive PII data, including customer names and email addresses. You need to create a representative sample of 1% of the data for model development, ensuring that the sample is anonymized and protects customer privacy. The sample must be reproducible for future model iterations.
Which of the following steps are most appropriate using Snowpark for Python and SQL?
  • A. Use the 'QUALIFY OVER (ORDER BY RANDOM()) (SELECT COUNT( ) 0.01 FROM CUSTOMER_TRANSACTIONS)' clause with SHA256 on sensitive columns directly within a CREATE TABLE AS statement to generate an anonymized sample. The function should return only 1 percentage of row.
  • B. Employ stratified sampling based on a customer segment column, then anonymize data. Use the TABLESAMPLE BERNOULLI function in SQL with a 1 percent sample rate. Apply SHA256 hashing to the 'customer_name' and 'email_addresS columns using SQL functions.
  • C. Use the 'SAMPLE clause in a SQL query to extract 1% of the rows, then apply SHA256 hashing to the 'customer_name' and 'email_addresS columns within Snowpark using a UDF. Seed the sampling for reproducibility.
  • D. Create a new table using 'CREATE TABLE AS SELECT statement combined with 'SAMPLE clause and SHA256 hashing functions in SQL to create the sample and anonymize data. Manually seed the random number generator in Python before executing the SQL statement via Snowpark.
  • E. Use Snowpark DataFrame's 'sample' function with a fraction of 0.01 and a fixed random seed. Before sampling, create a view that masks 'customer_name' and 'email_address' columns, and then sample from the view.
Answer: B,C
Explanation:
Options A and D are correct because they address both sampling and anonymization requirements while leveraging Snowflake's capabilities. Option A utilizes SAMPLE clause within a SQL query in Snowflake and then leverages UDF for SHA256 hashing of sensitive information. This is a practical and common data sampling/anonymization pattern. Option D employs stratified sampling based on a customer segment, TABLESAMPLE BERNOULLI and SHA256 hashing in SQL, which provides a solid anonymization and sampling strategy.Option B: Creating a view is a good practice, but it doesn't automatically anonymize the data, and directly sampling from the view without anonymization doesn't meet the security requirements. Option C: Manually seeding in Python doesn't guarantee reproducibility when SQL is executed separately, as Snowflake has its own random number generator. Option E does not guarantee reproduciblity, and the query complexity might introduce performance issues and is less readable compared to the other options.

NEW QUESTION # 129
You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?
  • A. Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.
  • B. Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
  • C. All of the above.
  • D. Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.
  • E. Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.
Answer: C
Explanation:
All options are potentially beneficial. Increasing the threshold (A) improves precision. Oversampling (B) addresses class imbalance. Cost-sensitive learning (C) penalizes misclassification. Feature engineering (D) improves discrimination. Therefore, the optimal solution may involve combining these strategies. Oversampling can be implemented using SQL and INSERT INTO statements in Snowflake, storing the oversampled data in a temporary table. Cost-sensitive learning might involve adjusting model weights or using a custom loss function (depending on the chosen model framework, potentially requiring integration with external ML tools).

NEW QUESTION # 130
You are deploying a time series forecasting model in Snowflake. You need to log the performance metrics (e.g., MAE, RMSE) of the model after each prediction run to the Snowflake Model Registry. Which of the following steps are necessary to achieve this?
  • A. Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.
  • B. You must create a custom logging solution outside of Snowflake using external services and then integrate those logs back into Snowflake via external functions and Model Registry APIs
  • C. Use the method with the 'metrics' parameter to log the metrics directly during model registration.
  • D. Use the method to log individual metrics to the Model Registry associated with a specific model version after the prediction run.
  • E. Leverage Snowflake's Event Tables to capture and store metrics data generated during model evaluation and prediction workflows and then access via stored procedures that log to the Model Registry.
Answer: C,D,E
Explanation:
Options A, C and D are correct. Option A: You can log metrics during model registration using the method with the 'metrics parameter. Option C: The method allows logging individual metrics associated with a model version after the prediction run. Option D: Event Tables are a good way to track and audit model usage and performance, allowing for capturing those logs. Logging to separate tables can be done, but is not as elegant. The preferred method is to use the model registry's functions. Option E, Custom logging solution requires additional overhead and complexity, when Snowflake provides native model registry logging features.

NEW QUESTION # 131
You are working with a Snowflake table named 'sensor readingS containing IoT sensor data'. The table has columns 'sensor id' , 'timestamp' , and 'reading value'. You observe that the 'reading value' column contains a significant number of missing values (represented as NULL). To prepare this data for a time series analysis, you need to impute these missing values. You have decided to use the 'LOCF' (Last Observation Carried Forward) method, filling the NULL values with the most recent non-NULL value for each sensor. In addition to LOCF, you also want to handle the scenario where a sensor has NULL values at the beginning of its data stream (i.e., no previous observation to carry forward). For these initial NULLs, you want to use a fixed default value of 0. Which of the following approaches, using either Snowpark for Python or a combination of Snowpark and SQL, correctly implements this LOCF imputation with a default value?
  • A.
  • B. All of the above
  • C.
  • D.
  • E.
Answer: A,C,E
Explanation:
Options A, B, and C all correctly implement LOCF imputation with a default value of 0 for initial NULLs. Option A first uses ignorenulls=TrueV within a window to perform LOCF and then uses 'fillnas to replace any remaining NULLs (the initial NULLs) with 0. Option B is the most concise, using 'coalesce' to combine the LOCF result with the default value of 0. 'coalesce' returns the first non-NULL value in a list of expressions. Option C implements the same logic using SQL within Snowpark. The function performs LOCF, and 'COALESCE provides the default value. Option D uses 'F.lag' , which retrieves the previous value, not the last value carried forward. Therefore, it will not perform LOCF correctly.

NEW QUESTION # 132
You are tasked with preparing a Snowflake table named 'PRODUCT REVIEWS' for sentiment analysis. This table contains columns like 'REVIEW ID, 'PRODUCT ID', 'REVIEW TEXT', 'RATING', and 'TIMESTAMP'. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the 'PRODUCT REVIEWS' table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.
  • A. creating a new table 'REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , 'REVIEW_ID' , and 'RATING') using 'CREATE TABLE AS SELECT. SQL: 'CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'
  • B. All of the above.
  • C. Using 'ALTER TABLE DROP COLUMN' to directly remove 'TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: 'ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
  • D. Creating a VIEW that only selects the 'REVIEW _ TEXT , 'REVIEW_ID', and 'RATING' columns, effectively hiding the irrelevant columns from the model. SQL: 'CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'
  • E. Dropping rows with 'NULL' values in REVIEW_TEXT and then dropping the 'PRODUCT_ID' and 'TIMESTAMP' columns using 'ALTER TABLE. SQL: 'CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
Answer: B
Explanation:
All of the options are valid strategies. A directly removes the irrelevant 'TIMESTAMP' column, saving storage. B creates a VIEW which offers a non-destructive way to filter columns. C creates a new table with only the necessary columns. D handles rows with missing review text and removes other irrelevant columns. Therefore, choosing 'All of the above' is the correct response. Depending on use case and downstream application we can make use of any of the options, hence more than one option is correct.

NEW QUESTION # 133
......
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