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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q234-Q239):

NEW QUESTION # 234
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A. Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
B. Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
C. Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
D. Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.
E. Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.

Answer: A,D

Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.

NEW QUESTION # 235
You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

A. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
B. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
C. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
D. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
E. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.

Answer: C

Explanation:
Option E correctly identifies the most appropriate metrics (Precision, Recall, Fl-score, AUC-ROC, and Log Loss) for evaluating a binary classification model, especially in the context of imbalanced datasets. It also correctly describes the focus of each metric. Accuracy can be misleading with imbalanced datasets. MSE and R-squared are for regression problems (Option B). Confusion Matrix is a table, and Options D, contains incorrect statement.

NEW QUESTION # 236
You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

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

Answer: C

Explanation:
Option E is the most efficient because it leverages Snowpark's built-in functions for string manipulation and conditional logic directly. It first removes all non-numeric characters using 'regexp_replace' and then uses 'iff (if and only if) to replace empty strings (resulting from cleaning) with NULL. This approach avoids using UDFs (User-Defined Functions), which can introduce overhead. Option B, although using 'regexp_replace' , requires an additional 'with_column' to handle empty strings after cleaning. Option A introduces UDF that decreases performance. Option C calls UDF with undefined 'call_udf function and 'snowflake-snowpark-python' library. Option D is missing dataframe and its transformation is not happening on top of Dataframe. Option E is preferrable over Option B, as it uses the single transformation.

NEW QUESTION # 237
You have a regression model deployed in Snowflake predicting customer churn probability, and you're using RMSE to monitor its performance. The current production RMSE is consistently higher than the RMSE you observed during initial model validation. You suspect data drift is occurring. Which of the following are effective strategies for monitoring, detecting, and mitigating this data drift to improve RMSE? (Select TWO)

A. Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.
B. Implement a process to continuously calculate and track the RMSE on a holdout dataset representing the most recent data, alerting you when the RMSE exceeds a predefined threshold.
C. Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.
D. Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.
E. Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.

Answer: B,D

Explanation:
Option A provides a proactive approach to monitoring the model's performance on new data and triggering alerts when the RMSE deteriorates. Option C helps identify changes in the input data that could be causing the drift. Option B is not ideal, as retraining on all historical data might not effectively adapt to recent drifts. Option D is inefficient and impractical for large datasets. Option E is incorrect because a high RMSE indicates poor performance and warrants investigation, not ignoring.

NEW QUESTION # 238
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 # 239
......

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DSA-C03 Certification Materials, Reliable DSA-C03 Test Notes

Reliable DSA-C03 Test Notes | DSA-C03 Passing Score

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q234-Q239):

Excellent Web-Based Snowflake DSA-C03 Practice Exam

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