SNOWFLAKE DSA-C03 - SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM PERFECT EXAM DETAILS

Snowflake DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam Perfect Exam Details

Snowflake DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam Perfect Exam Details

Blog Article

Tags: Exam DSA-C03 Details, Certification DSA-C03 Training, New DSA-C03 Test Questions, New DSA-C03 Test Blueprint, Reliable DSA-C03 Exam Dumps

As you know, when choosing a learning product, what we should value most is its content. The content of DSA-C03 study materials is absolutely rich. Our company collected a lot of information, and then our team of experts really spent a lot of energy to analyze and sort out this information. If you buy our DSA-C03 Exam Questions, then you will find that the information compiled is all about the keypoints and the latest. And we always keep on updating our DSA-C03 training quiz.

The DSA-C03 latest exam torrents have different classifications for different qualification examinations, which can enable students to choose their own learning mode for themselves according to the actual needs of users. The DSA-C03 exam questions offer a variety of learning modes for users to choose from, which can be used for multiple clients of computers and mobile phones to study online, as well as to print and print data for offline consolidation. Our reasonable price and DSA-C03 Latest Exam torrents supporting practice perfectly, as well as in the update to facilitate instant upgrade for the users in the first place, compared with other education platform on the market, the DSA-C03 test torrent can be said to have high quality performance, let users spend the least money to meet their maximum needs.

>> Exam DSA-C03 Details <<

Certification DSA-C03 Training | New DSA-C03 Test Questions

SurePassExams has designed DSA-C03 pdf dumps format that is easy to use. Anyone can download Snowflake DSA-C03 pdf questions file and use it from any location or at any time. Snowflake PDF Questions files can be used on laptops, tablets, and smartphones. Moreover, you will get actual Snowflake DSA-C03 Exam Questions in this Snowflake DSA-C03 pdf dumps file.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q217-Q222):

NEW QUESTION # 217
You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

Answer: A

Explanation:
The 'snowflake_cortex.sentiment' function returns a VARIANT containing the sentiment score and sentiment label. To extract the sentiment score as a float, you need to access the 'sentiment_score' field and cast it to FLOAT or NUMBER data type using Option D does this correctly, using the Snowflake's preferred casting syntax.Option A return type is incorrect, where it returns the Variant instead of FLOAT. Option B return type is correct but it doesnt cast the result to Float, which is not correct syntax as result is VARIANT. Option C is incorrect because of TO_NUMBER function. Option E result is the SENTIMENT Label instead sentiment score.


NEW QUESTION # 218
You are tasked with automating the retraining of a Snowpark ML model based on the performance metrics of the deployed model. You have a table 'MODEL PERFORMANCE that stores daily metrics like accuracy, precision, and recall. You want to automatically trigger retraining when the accuracy drops below a certain threshold (e.g., 0.8). Which of the following approaches using Snowflake features and Snowpark ML is the MOST robust and cost-effective way to implement this automated retraining pipeline?

  • A. Use a Snowflake stream on the 'MODEL_PERFORMANCE table to detect changes in accuracy, and trigger a Snowpark ML model training function using a PIPE whenever the accuracy drops below the threshold.
  • B. Create a Dynamic Table that depends on the 'MODEL PERFORMANCE table and materializes when the accuracy is below the threshold. This Dynamic Table refresh triggers a Snowpark ML model training stored procedure. This stored procedure saves the new model with a timestamp and updates a metadata table with the model's details.
  • C. Implement an external service (e.g., AWS Lambda or Azure Function) that periodically queries the "MODEL_PERFORMANCE table using the Snowflake Connector and triggers a Snowpark ML model training script via the Snowflake API.
  • D. Implement a Snowpark ML model training script that automatically retrains the model every day, regardless of the performance metrics. This script will overwrite the previous model.
  • E. Create a Snowflake task that runs every hour, queries the 'MODEL_PERFORMANCE table, and triggers a Snowpark ML model training script if the accuracy threshold is breached. The training script will overwrite the existing model.

Answer: B

Explanation:
Option D is the most robust and cost-effective solution. Using a Dynamic Table ensures that retraining is triggered only when necessary (when accuracy drops below the threshold). The Dynamic Table's materialization event then kicks off a Snowpark ML model training stored procedure that automatically retrains the model. This stored procedure saves the new model with a timestamp and updates a metadata table, allowing for version control. This eliminates unnecessary retraining runs (cost savings) and provides full lineage of models. Option A can be wasteful as it retrains even if it's not required. Option B using Stream & Pipes doesn't trigger model re-training after data accuracy breach. Option C doesn't account for model performance leading to unnecessary retrains. Option E introduces external dependencies and complexity that are best avoided within the Snowflake ecosystem.


NEW QUESTION # 219
You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

  • A. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
  • B. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
  • C. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.
  • D. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
  • E. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.

Answer: A,B,D

Explanation:
The correct answers are B, D, and E. B: One-hot encoding allows you to see the individual effect of each neighborhood. D: ICE plots reveal how the relationship between 'location' and predicted price varies for different individual instances, highlighting potential heterogeneity. E: A two-way PDP with 'location' and 'square_footage' helps understand if the effect of location is different for houses of different sizes. Removing 'location' (option A) might decrease performance if it's a relevant feature. Replacing it with average price (option C) introduces potential bias and data leakage if the historical data is used for both training and validation.


NEW QUESTION # 220
A data scientist is tasked with predicting house prices using Snowflake. They have a dataset stored in a Snowflake table called 'HOUSE PRICES' with columns such as 'SQUARE FOOTAGE, 'NUM BEDROOMS, 'LOCATION_ID, and 'PRICE. They choose a Random Forest Regressor model. Which of the following steps is MOST important to prevent overfitting and ensure good generalization performance on unseen data, and how can this be effectively implemented within a Snowflake-centric workflow?

  • A. Train the Random Forest model on the entire 'HOUSE PRICES table without splitting into training and validation sets, as this will provide the model with the most data.
  • B. Tune the hyperparameters of the Random Forest model (e.g., 'max_deptm, 'n_estimators') using cross-validation. You can achieve this by splitting the 'HOUSE PRICES table into training and validation sets using Snowflake's 'QUALIFY clause or temporary tables, then train and evaluate the model within a loop or stored procedure.
  • C. Increase the number of estimators (trees) in the Random Forest to the maximum possible value to capture all potential patterns, without cross validation.
  • D. Eliminate outliers without understanding the data properly to reduce noise.
  • E. Randomly select a small subset of the features (e.g., only use 'SQUARE FOOTAGE and 'NUM BEDROOMS) to simplify the model and prevent overfitting.

Answer: B

Explanation:
Hyperparameter tuning with cross-validation is crucial to prevent overfitting. By splitting the data into training and validation sets, we can evaluate the model's performance on unseen data and adjust the hyperparameters accordingly. Snowflake's 'QUALIFY' clause and temporary tables can be used to efficiently manage these splits. Using a maximum number of estimators without validation is prone to overfitting. Training on the entire dataset without validation provides no indication of generalization performance. Randomly selecting a subset of features may remove important predictors and eliminating outliers without proper investigation can skew your data and reduce the efficacy of the model.


NEW QUESTION # 221
You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

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

Answer: A

Explanation:
Option D is the most efficient and correct way to calculate RMSE. RMSE is the square root of the average of the squared differences between predicted and actual values. - p.predicted_price), 2)' calculates the squared difference. calculates the average of these squared differences. calculates the square root of the average, resulting in the RMSE. Option A is less efficient because it requires creating a temporary table. Option B and E are incorrect since they uses 'MEAN' which is unavailable in Snowflake and Exp/ln will return geometic mean instead of RMSE. Option C calculates the standard deviation of the differences, not the RMSE.


NEW QUESTION # 222
......

We are aware that taking the Snowflake DSA-C03 certification exam may be quite expensive. To save you money, we provide you with up to 1 year of free DSA-C03 exam questions updates. Moreover, you can check out the features of our SurePassExams's DSA-C03 practice exam material by downloading a free demo. We provide you with a Free DSA-C03 Exam Questions demo to assist you in making a decision that is well-informed. We are sure that by preparing with updated our Snowflake DSA-C03 exam questions you can get success and save both time and money.

Certification DSA-C03 Training: https://www.surepassexams.com/DSA-C03-exam-bootcamp.html

Hundreds of professionals worldwide examine and test every Snowflake DSA-C03 practice exam regularly, Snowflake Exam DSA-C03 Details It saves the client's time, Our after-sale service is very considerate and the clients can consult our online customer service about the price and functions of our DSA-C03 study materials and refund issues on the whole day and year, Snowflake Exam DSA-C03 Details As we all know, famous companies use certificates as an important criterion for evaluating a person when recruiting.

When not writing scripts, there is a vast assortment of duties to keep me from getting bored, Implement and manage threat protection, Hundreds of professionals worldwide examine and test every Snowflake DSA-C03 Practice Exam regularly.

2025 Exam DSA-C03 Details 100% Pass | The Best Certification SnowPro Advanced: Data Scientist Certification Exam Training Pass for sure

It saves the client's time, Our after-sale service is very considerate and the clients can consult our online customer service about the price and functions of our DSA-C03 study materials and refund issues on the whole day and year.

As we all know, famous companies use certificates as an important criterion for evaluating a person when recruiting, Exam-oriented DSA-C03 Q&A.

Report this page