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The client only needs 20-30 hours to learn our DSA-C03 learning questions: SnowPro Advanced: Data Scientist Certification Exam and then they can attend the test. Most people may devote their main energy and time to their jobs, learning or other important things and can't spare much time to prepare for the test. But if clients buy our DSA-C03 training materials they can not only do their jobs or learning well but also pass the test smoothly and easily because they only need to spare little time to learn and prepare for the test.

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are building a model training pipeline in Snowflake using Snowpark Python. You want to leverage a pre-trained model from Hugging Face Transformers for a text classification task, fine-tuning it with your own labeled data stored in a Snowflake table named 'training_data'. You've chosen the 'transformers' library and plan to use a 'transformers.pipeline' for inference. Which of the following code snippets, when integrated into your Snowpark Python application, will correctly download the pre trained model and tokenizer, prepare the data, perform fine-tuning, and then save the fine-tuned model to a Snowflake stage?

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

Question 2

You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

A.

B.

C.

D.

E.

Question 3

A financial institution aims to detect fraudulent transactions using a Supervised Learning model deployed in Snowflake. They have a dataset with transaction details, including amount, timestamp, merchant category, and customer ID. The target variable is 'is_fraudulent' (0 or 1). They are considering different Supervised Learning algorithms. Which of the following algorithms would be MOST suitable for this fraud detection task, considering the need for interpretability, scalability, and the potential for imbalanced classes, and what specific strategies can be employed within Snowflake to handle the class imbalance?

A. Support Vector Machine (SVM) with a radial basis function (RBF) kernel, as it can capture complex non-linear relationships without concern for interpretability.
B. K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.
C. Decision Tree or Random Forest, combined with techniques like oversampling the minority class (fraudulent transactions) within Snowflake using SQL or UDFs to balance the dataset before training. These models provide reasonable interpretability and can handle non-linear relationships effectively.
D. Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.
E. Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.

Question 4

You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

Question 5

Consider you are working on a credit risk scoring model using Snowflake. You have a table 'credit data' with the following schema: 'customer id', 'age', 'income', 'credit_score', 'loan_amount', 'loan_duration', 'defaulted'. You want to create several new features using Snowflake SQL to improve your model. Which combination of the following SQL statements will successfully create features for age groups, income-to-loan ratio, and interaction between credit score and loan amount using SQL in Snowflake? Choose all that apply.

A.

B.

C.

D.

E.

Solutions: