100% PASS 2025 DSA-C03: SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM ACCURATE NEW EXAM TOPICS

100% Pass 2025 DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Accurate New Exam Topics

100% Pass 2025 DSA-C03: SnowPro Advanced: Data Scientist Certification Exam Accurate New Exam Topics

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Tags: New DSA-C03 Exam Topics, DSA-C03 Real Brain Dumps, DSA-C03 Sample Questions Pdf, DSA-C03 Reliable Study Guide, DSA-C03 Reliable Exam Pdf

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

Answer: B

Explanation:
Option A correctly calculates precision and recall. Precision is calculated as True Positives / (True Positives + False Positives), and Recall is calculated as True Positives / (True Positives + False Negatives). The query in option A directly implements these formulas, where 'PREDICTED CHURN = 1 AND CHURN = 1' represents True Positives. Option B and E calculates accuracy. Option C calculates correlation. Option D calculates Precision and Recall for the negative class (non-churn).


NEW QUESTION # 41
A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

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

Answer: A

Explanation:
Both options B and D achieve the desired label encoding and handle NULL values correctly. Option B creates a new table based on the transformed dataset, which is a common approach for preserving the original data. Option D directly updates the existing table which could be appropriate depending on the team and security structure.


NEW QUESTION # 42
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. Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.
  • C. K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.
  • D. Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.
  • E. 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.

Answer: E

Explanation:
Decision Trees and Random Forests are well-suited for fraud detection due to their ability to handle non-linear relationships and provide interpretability. The class imbalance problem (where fraudulent transactions are much rarer than legitimate ones) is a common challenge in fraud detection. Oversampling the minority class or using techniques like SMOTE within Snowflake before training can significantly improve the model's performance. KNN is not well-suited for high-dimensional data or imbalanced datasets. SVM can be computationally expensive and lacks interpretability. Linear Regression is inappropriate for a classification problem. Naive Bayes makes strong independence assumptions that may not hold in fraud detection scenarios.


NEW QUESTION # 43
You are analyzing website clickstream data stored in Snowflake to identify user behavior patterns. The data includes user ID, timestamp, URL visited, and session ID. Which of the following unsupervised learning techniques, combined with appropriate data transformations in Snowflake SQL, would be most effective in discovering common navigation paths followed by users? (Choose two)

  • A. DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.
  • B. Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.
  • C. Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.
  • D. K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.
  • E. Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.

Answer: C,D

Explanation:
Sequence clustering is appropriate for identifying navigation paths because it considers the order of URLs visited within a session. Using Snowflake's LISTAGG function allows for creating the required sequential data. K-Means clustering can also be effective if relevant features are engineered from the URL data (e.g., frequency of visiting specific domains). Association rule mining is less suitable for identifying navigation paths as it focuses on co-occurrence rather than sequence. PCA followed by hierarchical clustering and DBSCAN are not well-suited for identifying sequential navigation paths from clickstream data. Option 'A' is incorrect because association rule mining directly on raw URL data is unlikely to be effective without prior sequence extraction. Option 'D' and 'E' are not suitable for this type of analysis.


NEW QUESTION # 44
You are working with a large dataset of customer transactions in Snowflake. The dataset contains columns like 'customer id' , 'transaction date', 'product category' , and 'transaction_amount'. Your task is to identify fraudulent transactions by detecting anomalies in spending patterns. You decide to use Snowpark for Python to perform time-series aggregation and feature engineering. Given the following Snowpark DataFrame 'transactions_df , which of the following approaches would be MOST efficient for calculating a 7-day rolling average of for each customer, while also handling potential gaps in transaction dates?

  • A. Use a stored procedure in SQL to iterate over each customer, calculate the rolling average using a cursor and conditional logic for handling missing dates.
  • B. Use a Snowpark Pandas UDF to calculate the rolling average for each customer after collecting all transactions for that customer into a Pandas DataFrame. Handle missing dates using Pandas functionality.
  • C. Use'window.partitionBy('customer_id').orderBy('transaction_date').rangeBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with a date range table joined to the transactions, filling in missing days before calculating the rolling average with 'transaction_amount' set to 0 for the inserted days.
  • D. Use a simple followed by a UDF to calculate the rolling average. Fill in missing dates manually within the UDF.
  • E. Use 'window.partitionBy('customer_id').orderBy('transaction_date').rowsBetween(-6, Window.currentRow)' within a 'select' statement and handle any missing dates using 'fillna()' after calculating the rolling average.

Answer: C

Explanation:
Option E is the MOST efficient. Using a date range table joined with the transactions DataFrame to fill in missing dates before calculating the rolling average using 'rangeBetween' is more performant than options that involve UDFs or procedural logic. Options A, C, and D introduce overhead with UDFs or stored procedures which can be slow for large datasets. Option B is less flexible in handling missing dates because 'rowsBetween' considers only the row number, not the actual date difference, potentially leading to inaccurate averages when there are gaps in dates.


NEW QUESTION # 45
......

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