Snowflake DSA-C03 valid exam dumps : SnowPro Advanced: Data Scientist Certification Exam

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Snowflake DSA-C03 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Scientist Certification Exam
Exam Number:	DSA-C03
Exam Price:	$375 USD
Exam Duration:	115 minutes
Related Certifications:	SnowPro Core Certification
Real Exam Qty:	65
Available Languages:	English
Certificate Validity Period:	2 years
Exam Format:	Multiple Choice, Multiple Select
Passing Score:	750/1000
Sample Questions:
Exam Way:	Online proctored exam or test center delivery through Pearson VUE
Pre Condition:	SnowPro Core Certification is recommended. Candidates should have 2+ years of hands-on experience with Snowflake in a production data science environment.
Official Syllabus URL:	https://learn.snowflake.com/en/certifications/snowpro-advanced-datascientistC03

Snowflake DSA-C03 Exam Syllabus Topics:

Questions Candidates Ask About Snowflake SnowPro Advanced: Data Scientist Certification

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are designing a feature engineering pipeline using Snowpark Feature Store for a fraud detection model. You have a transaction table in Snowflake. One crucial feature is the 'average_transaction_amount_last_7_days' for each customer. You want to implement this feature using Snowpark Python and materialize it in the Feature Store. You have the following Snowpark DataFrame 'transactions_df containing 'customer_id' and 'transaction_amount'. Which of the following code snippets correctly defines and registers this feature in the Snowpark Feature Store, ensuring efficient computation and storage?

A.

B.

C.

D.

E.

Question 2

You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

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

Question 3

You have a Snowflake table called 'website visits' with columns 'user id', 'visit_date', and You need to identify users who consistently spend a large amount of time on specific page URLs. You want to calculate the average time spent per user on each page URL and then find the top 10 page URLs where users, on average, spend the most time. Which of the following approaches is the MOST efficient and accurate for achieving this in Snowflake?

A.

B.

C.

D.

E.

Question 4

You are developing a real-time fraud detection system using Snowpark and deploying it as a Streamlit application connected to Snowflake. The system ingests transaction data continuously and applies a pre-trained machine learning model (stored as a binary file in Snowflake's internal stage) to score each transaction for fraud. You need to ensure the model loading process is efficient, and you're aiming to optimize performance by only loading the model once when the application starts, not for every single transaction. Which combination of approaches will BEST achieve this in a reliable and efficient manner, considering the Streamlit application's lifecycle and potential concurrency issues?

A. Load the model outside of the Streamlit application's execution context (e.g., in a separate script) and store it in a global variable. Access this global variable within the Streamlit application. This approach requires careful handling of concurrency.
B. Load the model within a try-except block and set the Snowpark session as a singleton that will guarantee model loads once for the entire application.
C. Use the 'st.cache_data' decorator in Streamlit to cache the loaded model and Snowpark session. Load the model directly from the stage within the cached function. This approach handles concurrency and ensures the model is only loaded once per session.
D. Use Python's built-in 'threading.Lock' to serialize access to the model loading code and the Snowpark session, preventing concurrent access from multiple Streamlit user sessions. Store the loaded model in a module-level variable.
E. Leverage the 'snowflake.snowpark.Session.read_file' to load the model binary directly into a Snowpark DataFrame and then convert to a Pandas DataFrame. Then, use the 'pickle' library for deserialization.

Question 5

You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)

A. Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.
B. Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.
C. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.
D. Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.
E. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.

Solutions: