EXAM SNOWFLAKE DSA-C03 MATERIAL, DSA-C03 RELIABLE EXAM MATERIALS

Exam Snowflake DSA-C03 Material, DSA-C03 Reliable Exam Materials

Exam Snowflake DSA-C03 Material, DSA-C03 Reliable Exam Materials

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q259-Q264):

NEW QUESTION # 259
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. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. 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.

Answer: A

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 # 260
A healthcare provider has a Snowflake table 'MEDICAL RECORDS containing patient notes stored as unstructured text in a column called 'NOTE TEXT. They want to identify different patient groups based on the topics discussed in these notes. They aim to use a combination of unsupervised and supervised learning. Which of the following represents a robust workflow to achieve this goal?

  • A. Use a Snowflake external function to call a pre-trained topic modeling model (e.g., BERTopic) hosted on Google Cloud A1 Platform. Assign topic probabilities to each patient note. Then, perform K-Means clustering on the topic probabilities to identify patient segments. No manual labeling is performed.
  • B. Export all 'NOTE TEXT data to an extemal system, use an existing NLP pipeline for topic modeling and manual labeling, then create a Snowflake UDF that replicates this entire pipeline internally.
  • C. Perform topic modeling on a sample of the 'NOTE TEXT data using a Snowflake Python UDF. Manually review the top documents for each identified topic, and assign labels describing the patient group represented by each topic. Train a supervised multi-label classification model (e.g., using scikit-learn's
  • D. Perform topic modeling (e.g., LDA) directly on the 'NOTE_TEXT column using a Python UDF in Snowflake. Manually label a subset of the resulting topics. Then, train a supervised classifier (e.g., Naive Bayes) to predict the identified topics for new patient notes.
  • E. MultiOutputClassifier wrapped around a Logistic Regression model) within Snowflake (using Snowpark), using the original 'NOTE TEXT as input features (TF-IDF or word embeddings) and the manually assigned topic labels as target variables. Use the trained model to classify the remaining patient notes into relevant patient groups.

Answer: C

Explanation:
Option D is the most comprehensive and practical. First, it uses unsupervised topic modeling to discover potential patient groups. Second, it uses manual labeling to create a supervised training dataset. Third, it trains a supervised multi-label classification model within Snowflake (using Snowpark), allowing for automated patient group assignment based on the text of their notes, leveraging TF-IDF or word embeddings for feature representation. This balances the efficiency of unsupervised learning with the accuracy of supervised learning. It also highlights Snowflake's ability to directly train and deploy models using Snowpark.


NEW QUESTION # 261
A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

  • A. Calculated as 'loan_amount I applicant_age' .
  • B. Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
  • C. Calculated as 'applicant_age applicant_age'.
  • D. Calculated as 'applicant_income I loan_amount'.
  • E. Calculated as 'applicant_age / applicant_income'.

Answer: A,B,D

Explanation:
The best combination provides diverse perspectives on financial stability. directly reflects the applicant's ability to cover the loan with their income. represents the loan burden relative to the applicant's age and can expose risk in younger, less established applicants. provides the most comprehensive view, including existing debt obligations from external data. "age_squared' and are less directly informative about repayment ability. They could potentially capture non-linear relationships, but 'age_squareff is more likely to introduce overfitting. relies on an external data source, making it a powerful, but potentially more complex, feature to implement.


NEW QUESTION # 262
You've created a Python stored procedure in Snowflake to train a model. The procedure successfully trains the model, saves it using 'joblib.dump' , and then attempts to upload the model file to an internal stage. However, the upload fails intermittently with a FileNotFoundErroN. The stage is correctly configured, and the stored procedure has the necessary privileges. Which of the following actions are MOST likely to resolve this issue? (Select TWO)

  • A. Before uploading the model to the stage, verify that the file exists using 'os.path.exists()' within the stored procedure. If the file does not exist, log an error and raise an exception.
  • B. Ensure that the Python packages used within the stored procedure (e.g., scikit-learn, joblib) are explicitly listed in the 'imports' clause of the 'CREATE PROCEDURE statement.
  • C. Before uploading the model to the stage, explicitly create the directory within the stage using 'snowflake.connector.connect()' and executing a 'CREATE DIRECTORY IF NOT EXISTS command on the stage. Then retry upload.
  • D. Use the fully qualified path for the model file when calling 'joblib.dump'. E.g., 'joblib.dump(model, '/tmp/model.joblib')' instead of 'joblib.dump(model, 'model .joblib')'.
  • E. Implement error handling within the Python code to catch the 'FileNotFoundError' and retry the file upload after a short delay using 'time.sleep()'. The stored procedure should retry the upload a maximum of 3 times before failing.

Answer: A,D

Explanation:
The ' FileNotFoundError' often occurs because the default working directory within the Snowflake Python execution environment is not what's expected, or the file isn't being saved where expected. Using a fully qualified path (Option B) ensures that the model is saved to a known location, typically '/tmpP. Verify if file exist (Option E) will ensure you have written model to a file and prevent exception before upload file to Stage. Options A is not relevant to the FileNotFoundError problem. Option C is just a workaround not a real solution. Option D makes no sense.


NEW QUESTION # 263
You are developing a fraud detection model in Snowflake. You've identified that transaction amounts and transaction frequency are key features. You observe that the transaction amounts are heavily right-skewed and the transaction frequencies have outliers. Furthermore, the model needs to be robust against seasonal variations in transaction frequency. Which of the following feature engineering steps, when applied in sequence, would be MOST appropriate to handle these data characteristics effectively?

  • A. 1. Apply a logarithmic transformation to the transaction amounts. 2. Apply a Winsorization technique to the transaction frequencies to handle outliers. 3. Calculate a rolling average of transaction frequency over a 7-day window.
  • B. 1. Apply a square root transformation to the transaction amounts. 2. Standardize the transaction frequencies using Z-score normalization. 3. Create dummy variables for the day of the week.
  • C. 1. Apply a Box-Cox transformation to the transaction amounts. 2. Apply a quantile-based transformation (e.g., using NTILE) to the transaction frequencies to map them to a uniform distribution. 3. Calculate the difference between the current transaction frequency and the average transaction frequency for that day of the week over the past year.
  • D. 1. Apply min-max scaling to the transaction amounts. 2. Remove outliers in transaction frequency using the Interquartile Range (IQR) method. 3. Calculate the cumulative sum of transaction frequencies.
  • E. 1. Apply a logarithmic transformation to the transaction amounts. 2. Replace outliers in transaction frequency with the mean value. 3. Create lag features of transaction frequency for the previous 7 days.

Answer: C

Explanation:
Option C is the most comprehensive solution. Box-Cox transformation is effective for skewed data and can handle negative values (if applicable after shifting). Quantile-based transformation maps the transaction frequencies to a uniform distribution, mitigating the impact of outliers. Calculating the difference between the current transaction frequency and the historical average for that day of the week effectively removes seasonality. Logarithmic transformation (A) is a good alternative to Box-Cox but might not be optimal for all skewness types. Winsorization (A) reduces the impact of outliers but doesn't necessarily normalize the data distribution. Standardization (B) is suitable if the data follows a normal distribution, but may not be effective with heavy outliers. Min-max scaling (D) preserves the data distribution, so it is not a remedy for skewed data. Removing outliers (D) can lead to information loss. Replacing outliers with the mean (E) can distort the data distribution.


NEW QUESTION # 264
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