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DSA-C03 Test Questions Vce Exam Pass Certify | Snowflake DSA-C03 Official Practice Test

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Snowflake DSA-C03 Official Practice Test, Test DSA-C03 Question

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
You are developing a churn prediction model and want to track its performance across different model versions using the Snowflake Model Registry. After registering a new model version, you need to log evaluation metrics (e.g., AUC, F 1-score) and custom tags associated with the training run. Assuming you have a registered model named 'churn_model' with version 'v2', which of the following code snippets demonstrates the correct way to log these metrics and tags using the Snowflake Python Connector and the 'ModelRegistry' API?

Answer: A

Explanation:
Option A is correct. It first retrieves the specific model version using , and then calls and 'set_tag' on the returned 'version' object. The other options either attempt to call these methods directly on the "ModelRegistry' object (incorrect as these are version-specific operations) or use incorrect syntax for accessing versions.

NEW QUESTION # 138
A pharmaceutical company is testing a new drug to lower blood pressure. They conduct a clinical trial with 200 patients. After treatment, the sample mean reduction in systolic blood pressure is 10 mmHg, with a sample standard deviation of 15 mmHg. You want to construct a 99% confidence interval for the true mean reduction in systolic blood pressure. Which of the following statements is most accurate concerning the appropriate distribution and critical value to use?

A. Use a t-distribution with 199 degrees of freedom, and the critical value is slightly larger than 2.576.
B. Use a z-distribution because we are estimating mean, and use a critical value of 1.96.
C. Use a t-distribution with 200 degrees of freedom, and the critical value is close to 2.576.
D. Use a chi-squared distribution with 199 degrees of freedom.
E. Use a z-distribution because the sample size is large (n > 30), and the critical value is approximately 2.576.

Answer: A

Explanation:
The correct answer is B. While the sample size is considered 'large' (n > 30), it's more accurate to use a t-distribution when the population standard deviation is unknown and estimated by the sample standard deviation. The t-distribution accounts for the added uncertainty from estimating the standard deviation. The degrees of freedom are n-1 = 199. The critical value for a 99% confidence interval with a t-distribution and 199 degrees of freedom will be slightly larger than the z-score of 2.576. Option A is incorrect because using t-distribution is slightly better. Option C is incorrect because chi-squared distribution is for variance/standard deviation. Option D is incorrect since 1.96 is z score for 95%. Option E is incorrect as the degrees of freedom should be n-1.

NEW QUESTION # 139
You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A. Precision, Recall, and Fl-score.
B. Precision and Fl-score.
C. Accuracy and Recall.
D. Accuracy and ROC AUC.
E. ROC AUC and Recall.

Answer: A

Explanation:
Option D (Precision, Recall, and Fl-score) is the most comprehensive. In an imbalanced classification problem like fraud detection, accuracy alone is misleading because a model can achieve high accuracy by simply predicting the majority class (non-fraud) for all instances. Precision measures the proportion of correctly identified fraudulent transactions out of all transactions flagged as fraudulent. Recall measures the proportion of correctly identified fraudulent transactions out of all actual fraudulent transactions. F 1-score is the harmonic mean of precision and recall, providing a balanced measure of the model's performance. All three metrics are needed to comprehensively evaluate a fraud detection model on an imbalanced dataset.

NEW QUESTION # 140
You are developing a fraud detection model in Snowflake using Snowpark Python. You've iterated through multiple versions of the model, each with different feature sets and algorithms. To ensure reproducibility and easy rollback in case of performance degradation, how should you implement model versioning within your Snowflake environment, focusing on the lifecycle step of Deployment & Monitoring?

A. Implement a custom versioning system using Snowflake stored procedures that track model versions and automatically deploy the latest model by overwriting the existing one. The prior version gets deleted.
B. Utilize Snowflake's Time Travel feature to revert to previous versions of the model artifact stored in a Snowflake stage.
C. Store the trained models directly in external cloud storage (e.g., AWS S3, Azure Blob Storage) with explicit versioning enabled on the storage layer, and update Snowflake metadata (e.g., in a table) to point to the current model version. Use a UDF to load the correct model version.
D. Store each model version as a separate Snowflake table, containing serialized model objects and metadata like training date, feature set, and performance metrics. Use views to point to the 'active' version.
E. Only maintain the current model version. If any problems arise, retrain a new model and redeploy it to replace the faulty one.

Answer: C

Explanation:
Storing models in external stages with versioning allows you to easily manage different model versions. Snowflake metadata points to the correct version, and UDFs can load them. Time Travel is useful, but is not ideal for large binary files. Option A is possible, but leads to potentially large and unwieldy Snowflake tables. Option C is not recommended as manual processes can lead to human errors and overwriting active models directly without proper model management creates deployment risks. Deleting older models (option E) prevents rollback.

NEW QUESTION # 141
You are building a machine learning pipeline in Snowflake using Snowpark Python. You have completed the data preparation and feature engineering steps and now need to train a model. You want to track the performance of different model versions and hyperparameters using MLflow. You are considering these deployment strategies. Which of the deployment strategies allows automatic logging of metrics, parameters, and model artifacts to MLflow for each training run without requiring explicit MLflow logging code?

A. Train the model using Snowpark's DataFrame API directly in a Snowflake worksheet. Manually create a log file with metrics and model parameters and upload it to a Snowflake stage.
B. Train the model locally on your development machine and manually log metrics and artifacts to MLflow using the MLflow API. Then, deploy the trained model to Snowflake as a UDF or stored procedure.
C. Use the Snowpark MLAPI and its integration with MLflow's autologging feature. Enable autologging before starting the training run. Deploy the model to Snowflake as a UDF.
D. Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
E. Train the model within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.

Answer: C

Explanation:
The Snowpark MLAPI, combined with MLflow's autologging feature, is designed to automatically log metrics, parameters, and model artifacts to MLflow without requiring explicit logging code. The autologging functionality is triggered when enabled before the training process begins. Other options require manual logging, lack built-in MLflow integration, or don't fully leverage the Snowpark ML capabilities.

NEW QUESTION # 142
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