VALID SNOWFLAKE DSA-C03 EXAM QUESTIONS ARE CONVENIENTLY AVAILABLE IN PDF FORMAT

Valid Snowflake DSA-C03 Exam Questions are Conveniently Available in PDF Format

Valid Snowflake DSA-C03 Exam Questions are Conveniently Available in PDF Format

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Tags: DSA-C03 Latest Exam Questions, Test DSA-C03 Free, New DSA-C03 Exam Online, Exam DSA-C03 Overviews, Trustworthy DSA-C03 Dumps

TestPDF SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) practice test material covers all the key topics and areas of knowledge necessary to master the Snowflake Certification Exam. Experienced industry professionals design the DSA-C03 exam questions and are regularly updated to reflect the latest changes in the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam. In addition, TestPDF offers three different formats of practice material which are discussed below.

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q221-Q226):

NEW QUESTION # 221
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. 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.
  • C. 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.
  • D. 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.
  • 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: 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 # 222
You are building a machine learning model using Snowflake data to predict customer churn. Your dataset includes a 'CUSTOMER TYPE column with the following possible values: 'New', 'Returning', and 'VIP'. You need to perform one-hot encoding on this column. Which of the following Snowflake SQL queries correctly implements one-hot encoding for the 'CUSTOMER TYPE column, creating separate binary columns for each customer type ('IS NEW', 'IS RETURNING', 'IS VIP')?

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

Answer: B,C,E

Explanation:
Options A, B, and C are all valid ways to perform one-hot encoding in Snowflake. Option A uses the standard 'CASE statement, Option B leverages the 'IFF function (inline IF), and Option C uses 'DECODE , all achieving the same result of creating binary indicators for each category. Option D is incorrect because it uses GET DDL, which retrieves DDL statements, not for comparison. Option E is incorrect because it does not represent three seperate columns of binary columns for each customer type. Therefore, options A, B, and C are the correct approaches to generate separate binary columns for one-hot encoding.


NEW QUESTION # 223
A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

  • A. The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
  • B. Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
  • C. The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.
  • D. The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
  • E. Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.

Answer: C,D

Explanation:
Options B and C are correct. Bootstrapping relies on the assumption that the original sample is representative of the population. If it isn't, the bootstrap distribution will reflect the biases of the sample. Also certain statistics, particularly those sensitive to outliers or with high variance, can produce bootstrap distributions that differ significantly from the original data distribution. Option A is incorrect because the bootstrap distribution doesn't necessarily have to be same as sample distribution. Option D is incorrect since Bootstrapping makes no assumptions regarding the distribution of original dataset and can be used for any data distribution. Option E is not correct. Bootstrapping is not always accurate and relies on assumptions to perform correctly.


NEW QUESTION # 224
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. 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.
  • B. Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.
  • 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. 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

Answer: A,C,D

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 # 225
You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

Answer: D

Explanation:
Option C is correct. It uses 'SYSTEM$GET FILE(@IMAGE STAGE/image.jpg', to retrieve the image file as a binary object and then to encode it as a base64 string before passing it to the 'CLASSIFY_IMAGE external function. Option A is incorrect as it attempts to directly use a file format which is not relevant for sending the image content. Option B is incorrect because the image needs to be base64 encoded first. Option D is incorrect as it converts binary to JSON, which is not the required format. Option E is incorrect because BLOB TO BASE64' doesn't exists in Snowflake. TO BASE64 is correct method.


NEW QUESTION # 226
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