SnowPro Advanced DSA-C03 pass4sure braindumps & DSA-C03 practice pdf test

Rated:

, 0 Comments

Total visits: 5

Posted on: 06/17/25

The DSA-C03 test material, in order to enhance the scientific nature of the learning platform, specifically hired a large number of qualification exam experts, composed of product high IQ team, these experts by combining his many years teaching experience of DSA-C03 quiz guide and research achievements in the field of the test, to exam the popularization was very complicated content of SnowPro Advanced: Data Scientist Certification Exam exam dumps. Expert team can provide the high quality for the DSA-C03 Quiz guide consulting for you to pass the DSA-C03 exam.

Some people are inclined to read paper materials. Do not worry. Our company has already taken your thoughts into consideration. Our PDF version of the DSA-C03 practice materials support printing on papers. All contents of our DSA-C03 Exam Questions are arranged reasonably and logically. In addition, the word size of the DSA-C03 study guide is suitable for you to read. And you can take it conveniently.

>> DSA-C03 Upgrade Dumps <<

Snowflake DSA-C03 Learning Materials - DSA-C03 Vce Test Simulator

When you first contacted us with DSA-C03 quiz torrent, you may be confused about our DSA-C03 exam question and would like to learn more about our products to confirm our claims. We have a trial version for you to experience. If you encounter any questions about our DSA-C03 learning materials during use, you can contact our staff and we will be happy to serve for you. Maybe you will ask if we will charge an extra service fee. We assure you that we are committed to providing you with guidance on DSA-C03 Quiz torrent, but all services are free of charge. As for any of your suggestions, we will take it into consideration, and effectively improve our DSA-C03 exam question to better meet the needs of clients. In the process of your study, we have always been behind you and are your solid backing. This will ensure that once you have any questions you can get help in a timely manner.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q223-Q228):

NEW QUESTION # 223
A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

Answer: D

Explanation:
Option D is the most efficient and accurate because it uses a single SQL query to calculate the MSE directly. It avoids using cursors or procedural logic, which are less performant in Snowflake. It uses SUM to calculate the sum of squared errors and COUNT( ) to get the total number of records, then divides to obtain the average (MSE). Option B calculates the average of power, that is wrong mathematical operation, Option A is correct from mathematical point but slow because of cursor and not following Snowflake best practices, option C is using JavaScript which is also valid, but Snowflake recommends to use SQL when possible for performance, and option E is using external python for model calculation, that not best for this scenarios.

NEW QUESTION # 224
Which of the following statements are TRUE regarding the 'Data Understanding' and 'Data Preparation' steps within the Machine Learning lifecycle, specifically concerning handling data directly within Snowflake for a large, complex dataset?

A. Data Preparation in Snowflake can involve feature engineering using SQL functions, creating aggregated features with window functions, and handling missing values using 'NVL' or 'COALESCE. Furthermore, Snowpark Python provides richer data manipulation using DataFrame APIs directly on Snowflake data.
B. Data Understanding primarily involves identifying potential data quality issues like missing values, outliers, and inconsistencies, and Snowflake features like 'QUALIFY and 'APPROX TOP can aid in this process.
C. Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
D. During Data Preparation, you should always prioritize creating a single, wide table containing all possible features to simplify the modeling process.
E. The 'Data Understanding' step is unnecessary when working with data stored in Snowflake because Snowflake automatically validates and cleans the data during ingestion.

Answer: A,B

Explanation:
Data Understanding is crucial for identifying data quality issues using tools such as 'QUALIFY' and 'APPROX TOP Data Preparation within Snowflake using SQL and Snowpark Python enables efficient feature engineering and data cleaning. Option C is incorrect because Snowflake doesn't automatically validate and clean your data. Option D is incorrect as leveraging Snowflake's compute for data preparation alongside Snowpark can drastically increase speed. Option E is not desirable, feature selection is important, and feature stores help in organization.

NEW QUESTION # 225
A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

Answer: B,C,E

Explanation:
Options B, D and E correctly calculate the average daily sales for each month and year. Options B uses 'COALESCE' to replace NULL SALE_AMOUNT values with 0 before calculating the average. Option D utilizes ' NVL', which is a synonym for COALESCE in Snowflake. Option E uses ZEROIFNULL' which is another way to handle NULL values. Option A does not handle NULL values, potentially skewing the average. Option C incorrectly uses "TO_CHAR which results in string format for date, but that is fine, it also tries to use 'IFF which is acceptable to handle Null, but SIFF function may lead to string conversion issues when calculating the average.

NEW QUESTION # 226
You are a data scientist working for a retail company using Snowflake. You're building a linear regression model to predict sales based on advertising spend across various channels (TV, Radio, Newspaper). After initial EDA, you suspect multicollinearity among the independent variables. Which of the following Snowflake SQL statements or techniques are MOST appropriate for identifying and addressing multicollinearity BEFORE fitting the model? Choose two.

A. Implement Principal Component Analysis (PCA) using Snowpark Python to transform the independent variables into uncorrelated principal components and then select only the components explaining a certain percentage of the variance.
B. Calculate the Variance Inflation Factor (VIF) for each independent variable using a user-defined function (UDF) in Snowflake that implements the VIF calculation based on R-squared values from auxiliary regressions. This requires fitting a linear regression for each independent variable against all others.
C. Use ' on each independent variable to estimate its uniqueness. If uniqueness is low, multicollinearity is likely.
D. Generate a correlation matrix of the independent variables using 'CORR aggregate function in Snowflake SQL and examine the correlation coefficients. Values close to +1 or -1 suggest high multicollinearity.
E. Drop one of the independent variable randomly if they seem highly correlated.

Answer: B,D

Explanation:
Multicollinearity can be identified by calculating the VIF for each independent variable. VIF is calculated by regressing each independent variable against all other independent variables and calculating 1/(1-RA2), where RA2 is the R-squared value from the regression. A high VIF suggests high multicollinearity. Correlation matrices generated with 'CORR can also reveal multicollinearity by showing pairwise correlations between independent variables. PCA using Snowpark is also a viable option, but less direct than VIF and correlation matrix analysis for identifying multicollinearity. APPROX_COUNT_DISTINCT is not directly related to identifying multicollinearity. Randomly dropping variables will also lead to data loss.

NEW QUESTION # 227
A financial institution wants to use Snowflake Cortex to analyze customer reviews and feedback extracted from various online sources to gauge customer sentiment towards their new mobile banking application. The goal is to identify positive, negative, and neutral sentiments, and also extract key phrases that drive these sentiments. Which of the following steps represent a viable workflow for achieving this using Snowflake Cortex and related functionalities?

A. 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKML.PREDICT' function with the appropriate task-specific model to determine the sentiment score for each review. 3. Further fine-tune the sentiment model with customer review data to improve the score and accuracy.
B. 1. Create a Streamlit application hosted externally that connects to the Snowflake database. 2. The Streamlit app uses a Python library like 'transformers' to perform sentiment analysis and key phrase extraction on the customer reviews read from Snowflake. 3. The results are then written back to a separate Snowflake table.
C. 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKE.ML.PREDICT' function with a sentiment analysis model to determine the overall sentiment score for each review. 3. Apply a separate key phrase extraction model via 'SNOWFLAKE.ML.PREDICT' to identify important keywords in the reviews.
D. 1. Ingest the customer reviews into a Snowflake table. 2. Create a custom JavaScript UDF that calls the Snowflake Cortex 'COMPLETE' endpoint with a prompt that asks for both sentiment and key phrases. 3. Store the results in a new Snowflake table.
E. 1. Ingest the customer reviews into a Snowflake table. 2. Use Snowflake's built-in 'NLP_SENTIMENT' function (if available) or a similar UDF based on a pre- trained sentiment analysis model to get the sentiment score. 3. Use regular expressions in SQL to extract key phrases based on frequency and context.

Answer: C

Explanation:
Option A is the most viable workflow. It leverages Snowflake Cortex directly to perform both sentiment analysis and key phrase extraction. By using the 'SNOWFLAKE.ML.PREDICT' function with appropriate models, it keeps the processing within the Snowflake environment and avoids the need for external dependencies or custom coding (as in options B and C). The rest of the options are less effective because they involve use third party components when Snowflake Cortex readily has modules that can do what is required.

NEW QUESTION # 228
......

PrepAwayPDF wants to win the trust of SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam candidates at any cost. To achieve this objective PrepAwayPDF is offering real, updated, and error-free SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam dumps in three different formats. These SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam questions formats are PrepAwayPDF Snowflake DSA-C03 dumps PDF files, desktop practice test software, and web-based practice test software.

DSA-C03 Learning Materials: https://www.prepawaypdf.com/Snowflake/DSA-C03-practice-exam-dumps.html

The quality of our DSA-C03 dumps torrent is excellent and it meets international certification exam standards, Fortunately, you find us and you find our DSA-C03 test cram may be their savior so that you can clear exam and obtain certification ahead of other competitor, With the help of our DSA-C03 practice dumps, you will be able to feel the real exam scenario, Snowflake DSA-C03 Upgrade Dumps But we guarantee to you if you fail in we will refund you in full immediately and the process is simple.

State the effect of using a variable or array element DSA-C03 of any kind, when no explicit assignment has been made to it, In this commentary, Visual Basic expert Scot Hillier discusses how to identify and DSA-C03 Learning Materials mitigate the inherent risks associated with software development to turn the odds in your favor.

SnowPro Advanced: Data Scientist Certification Exam latest study dumps & DSA-C03 simulated test torrent

The quality of our DSA-C03 Dumps Torrent is excellent and it meets international certification exam standards, Fortunately, you find us and you find our DSA-C03 test cram may be their savior so that you can clear exam and obtain certification ahead of other competitor.

With the help of our DSA-C03 practice dumps, you will be able to feel the real exam scenario, But we guarantee to you if you fail in we will refund you in full immediately and the process is simple.

Windows, Mac, iOS, Android, and Linux support this DSA-C03 practice exam.

Tags: DSA-C03 Upgrade Dumps, DSA-C03 Learning Materials, DSA-C03 Vce Test Simulator, Valid Dumps DSA-C03 Book, New DSA-C03 Exam Name

Comments

There are still no comments posted ...

Rate and post your comment

Username:
Password:
Forgotten password?

Most Popular

SnowPro Advanced DSA-C03 pass4sure braindumps & DSA-C03 practice pdf test

Snowflake DSA-C03 Learning Materials - DSA-C03 Vce Test Simulator

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q223-Q228):

SnowPro Advanced: Data Scientist Certification Exam latest study dumps & DSA-C03 simulated test torrent

Login