100% PASS 2025 UNPARALLELED SNOWFLAKE DSA-C03: LATEST SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM LEARNING MATERIALS

100% Pass 2025 Unparalleled Snowflake DSA-C03: Latest SnowPro Advanced: Data Scientist Certification Exam Learning Materials

100% Pass 2025 Unparalleled Snowflake DSA-C03: Latest SnowPro Advanced: Data Scientist Certification Exam Learning Materials

Blog Article

Tags: Latest DSA-C03 Learning Materials, Latest DSA-C03 Examprep, Latest DSA-C03 Test Labs, DSA-C03 Latest Learning Material, DSA-C03 Exam Cram Review

The content of our study materials is easy to be mastered and has simplified the important information. Our DSA-C03 test questions convey more important information with less questions and answers and thus make the learning relaxing and efficient. The software boosts self-learning and self-assessment functions to check the results of the learning. The software can help the learners find the weak links and deal with them. Our DSA-C03 Test Questions boost timing function and the function to stimulate the exam. Our DSA-C03 exam materials have simplified the complicated notions and add the instances , the stimulation and the diagrams to explain any contents which are hard to explain. So you can enjoy the service of high quality and pass the exam successfully.

We provide you with free demo for you to have a try before buying DSA-C03 exam bootcamp, so that you can have a deeper understanding of what you are going to buy. What’s more, DSA-C03 exam materials contain most of the knowledge points for the exam, and you can pass the exam as well as improve your professional ability in the process of learning. In order to let you obtain the latest information for the exam, we offer you free update for 365 days after buying DSA-C03 Exam Materials, and the update version will be sent to your email automatically. You just need to check your email for the latest version.

>> Latest DSA-C03 Learning Materials <<

Latest DSA-C03 Examprep, Latest DSA-C03 Test Labs

Therefore, you have the option to use Snowflake DSA-C03 PDF questions anywhere and anytime. Prep4sureExam SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) dumps are designed according to the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) certification exam standard and have hundreds of questions similar to the actual DSA-C03 Exam. Prep4sureExam Snowflake web-based practice exam software also works without installation.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
A data scientist is using Snowflake to perform anomaly detection on sensor data from industrial equipment. The data includes timestamp, sensor ID, and sensor readings. Which of the following approaches, leveraging unsupervised learning and Snowflake features, would be the MOST efficient and scalable for detecting anomalies, assuming anomalies are rare events?

  • A. Implement an Isolation Forest model. Train the Isolation Forest model on a representative sample of the sensor data and create UDF to score each row in snowflake.
  • B. Apply Autoencoders to the sensor data using a Snowflake external function. Data points are considered anomalous if the reconstruction error from the autoencoder exceeds a certain threshold.
  • C. Calculate the moving average of sensor readings over a fixed time window using Snowflake SQL and flag data points that deviate significantly from the moving average as anomalies. No ML model needed.
  • D. Use a Support Vector Machine (SVM) with a radial basis function (RBF) kernel trained on the entire dataset to classify data points as normal or anomalous. Implement the SVM model as a Snowflake UDF.
  • E. Use K-Means clustering to group sensor readings into clusters and identify data points that are far from the cluster centroids as anomalies. No model training necessary.

Answer: A

Explanation:
Isolation Forest is specifically designed for anomaly detection and performs well with high-dimensional data. Because anomalies are defined as 'few and different,' Isolation Forest builds an ensemble of trees and isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. Anomalies require fewer splits to be isolated and consequently have a shorter path length in the tree, where this path length is the measurement of 'solation'. It is scalable and well-suited for large datasets within Snowflake, especially when integrated via a UDF.SVM is computationally intensive. K-Means only effective when anomalies are caused by shifted data, no individual outliers. Calculationg the moving average is quick to compute, and has a faster throughput, but is extremely sensitive to outliers. Option A is computationally expensive and may not scale well. Options C is suitable for a high level initial assessment, and not for accuracy. Option E, Autoencoders would have difficulty training and might not perform well.


NEW QUESTION # 43
You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including 'CUSTOMER AGE', 'MONTHLY SPEND', and 'NUM CALLS'. After performing an initial linear regression, you suspect that the relationship between 'CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of "CUSTOMER AGE (specifically, 'CUSTOMER AGE SQUARED') to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?

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

Answer: A

Explanation:
Creating a VIEW (option C) is the BEST approach for several reasons. It doesn't modify the underlying data, which is crucial for data govemance and prevents unintended side effects. The feature is calculated on-the-fly whenever the view is queried, ensuring that the feature is always up-to-date if the underlying changes. Options A, D, and E permanently alter the table, potentially leading to data redundancy and requiring manual updates if the column changes. Option B creates a temporary table, which is suitable for short-lived experiments but not ideal for a feature that will be used repeatedly. Using 2) is equivalent to CUSTOMER_AGE CUSTOMER_AGE. Views are efficient because Snowflake's query optimizer can often push down computations into the underlying table. Option C also avoids needing to manage the lifecycle of updated calculated columns.


NEW QUESTION # 44
You are building a churn prediction model for a telecommunications company using Snowflake and Snowpark ML. You have trained a Gradient Boosting Machine (GBM) model and want to understand the feature importance to identify key drivers of churn. You've used SHAP (SHapley Additive exPlanations) values to explain individual predictions. Given a customer with a high churn risk, you observe that the 'monthly_charges' feature has a significantly large negative SHAP value for that specific prediction. Which of the following statements best interprets this observation in the context of feature impact?

  • A. The 'monthly_charges' feature has no impact on the customer's churn probability.
  • B. The negative SHAP value indicates that 'monthly_charges' is negatively correlated with all customers' churn probability, irrespective of their individual profile.
  • C. The negative SHAP value suggests 'monthly_charges' interacts with other features. Its precise impact is conditional and cannot be generalized without further analysis of feature interaction effects with SHAP values.
  • D. Increasing 'monthly_charges' for this customer is likely to increase their probability of churning.
  • E. Increasing 'monthly_charges' for this customer is likely to decrease their probability of churning.

Answer: D

Explanation:
A negative SHAP value for a specific prediction indicates that the feature's value pushed the prediction lower compared to the average prediction. In the context of churn, a lower prediction means a higher probability of churning. Thus, an increase in 'monthly_charges' for this specific customer, given their other features, is likely to increase their churn probability. Option E is partially correct as feature interactions are important but B is the best immediate interpretation.


NEW QUESTION # 45
You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

  • A. MinMaxScaler (Min-Max scaling)
  • B. RobustScaler (using interquartile range)
  • C. Power Transformer (Yeo-Johnson or Box-Cox)
  • D. No scaling is needed as tree-based models are robust to skewed data.
  • E. StandardScaler (Z-score normalization)

Answer: B,C

Explanation:
RobustScaler is suitable for handling outliers as it uses the interquartile range, which is less sensitive to extreme values than the mean and standard deviation used by StandardScaler. PowerTransformer can also be useful for transforming skewed data to a more Gaussian-like distribution, which can improve the performance of some machine learning models. While tree-based models are generally more robust to skewed data than other models, scaling can still improve convergence speed or performance, especially when combined with other preprocessing techniques or models that are sensitive to feature scaling. Therefore, E is not a great choice. Using RobustScaler and PowerTransformer will lead to a better performance of model.


NEW QUESTION # 46
You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

  • A. Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
  • B. Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
  • C. Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.
  • D. Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
  • E. Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.

Answer: A,E

Explanation:
Options A and E provide the most robust and scalable solutions. A UDF offers flexibility and reusability for data cleaning within Snowpark (Option A). Option E leverages Snowflake's data loading capabilities to clean data during ingestion and adds a UDF for cleaning existing data providing a comprehensive approach. Using a UDF written in Python and used within Snowpark leverages the power of Python's regular expression capabilities and the distributed processing of Snowpark. Handling data transformations during ingestion with Snowflake's built- in COPY INTO with transformation is highly efficient. Option B is less scalable and maintainable for complex formatting. Option C is viable but executing SQL stored procedures from Snowpark Python loses some of the advantages of Snowpark. Option D addresses data masking not data transformation.


NEW QUESTION # 47
......

Prep4sureExam 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, Prep4sureExam offers three different formats of practice material which are discussed below.

Latest DSA-C03 Examprep: https://www.prep4sureexam.com/DSA-C03-dumps-torrent.html

Our latest training materials about Snowflake Latest DSA-C03 Examprep Latest DSA-C03 Examprep - SnowPro Advanced: Data Scientist Certification Exam passleader review is developed by our professional team's constantly study of Latest DSA-C03 Examprep - SnowPro Advanced: Data Scientist Certification Exam free dumps certification, And more than that, we offer DSA-C03 quiz torrent with most desirable benefits for users like you, Our DSA-C03 study materials sove this problem perfectly for you with high-efficience and you will know if you can just have a try!

Developing an affiliate program that creates a collaborative DSA-C03 Exam Cram Review commerce network is one of the most powerful ways today to market online, Modernizing the Data Center with Windows Server and Hybrid Cloud is the definitive guide to using Windows DSA-C03 Server, Microsoft Azure, and related Microsoft technologies to achieve this transformation in your own environment.

Highly Authoritative DSA-C03 Learning Question Will Help You Pass Your Exam - Prep4sureExam

Our latest training materials about Snowflake SnowPro Advanced: Data Scientist Certification Exam Latest DSA-C03 Test Labs passleader review is developed by our professional team's constantly study of SnowPro Advanced: Data Scientist Certification Exam free dumps certification.

And more than that, we offer DSA-C03 Quiz torrent with most desirable benefits for users like you, Our DSA-C03 study materials sove this problem perfectly for you with high-efficience and you will know if you can just have a try!

All these SnowPro Advanced: Data Scientist Certification Exam dumps features are designed to assist you in DSA-C03 SnowPro Advanced: Data Scientist Certification Exam exam preparation and enable you to pass the exam with flying colors, And our DSA-C03 exam quiz is a bountiful treasure you cannot miss.

Report this page