PRINTABLE DSA-C03 PDF | DSA-C03 RELIABLE TEST VOUCHER

Printable DSA-C03 PDF | DSA-C03 Reliable Test Voucher

Printable DSA-C03 PDF | DSA-C03 Reliable Test Voucher

Blog Article

Tags: Printable DSA-C03 PDF, DSA-C03 Reliable Test Voucher, New DSA-C03 Test Pdf, New DSA-C03 Exam Guide, Instant DSA-C03 Discount

We have the DSA-C03 bootcamp , it aims at helping you increase the pass rate , the pass rate of our company is 98%, we can ensure that you can pass the exam by using the DSA-C03 bootcamp. We have knowledge point as well as the answers to help you finish the traiing materials, if you like, it also has the offline version, so that you can continue the study at anytime

If you want to DSA-C03 practice testing the product of VCEPrep, feel free to try a free demo and overcome your doubts. A full refund offer according to terms and conditions is also available if you don't clear the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) practice test after using the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam product. Purchase VCEPrep best DSA-C03 study material today and get these stunning offers.

>> Printable DSA-C03 PDF <<

We provide 100% premium Snowflake DSA-C03 exam questions

Snowflake certification is very helpful, especially the DSA-C03 which is recognized as a valid qualification in this industry. So far, DSA-C03 free download pdf has been the popular study material many candidates prefer. DSA-C03 questions & answers can assist you to make a detail study plan with the comprehensive and detail knowledge. Besides, we have money refund policy to ensure your interest in case of your failure in DSA-C03 Actual Test. Additional, if you have any needs and questions about the Snowflake test dump, our 24/7 will always be here to answer you.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

  • A. Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
  • B. Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.
  • C. Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.
  • D. Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.
  • E. Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.

Answer: C,D

Explanation:
Vectorized UDFs (B) are optimized for performance by processing data in batches, significantly reducing the overhead associated with individual row processing. Repartitioning (E) ensures data is evenly distributed across nodes, allowing for parallel execution of UDFs and reducing skew, which can lead to performance bottlenecks. Java UDFs while faster than unoptimized Python UDFs, require extra work and maintenance, while vectorized UDFs are a more straight forward solution within Snowpark Python. Using temporary tables (D) could add overhead rather than reducing it. Using External functions (C) is a complex solution for what can be handled natively.


NEW QUESTION # 69
A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?

  • A. Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.
  • B. Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.
  • C. Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
  • D. Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.
  • E. Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.

Answer: C

Explanation:
Option C is the most effective. Using a variance threshold directly addresses multicollinearity by removing redundant features. Creating a geographical region feature from 'state' reduces dimensionality and is more manageable than one-hot encoding for high cardinality features. A custom UDF can be used for efficient regional mapping. While PCA can reduce dimensionality, it can also make the features less interpretable. Target encoding (B) can introduce target leakage if not handled carefully. VIF calculation (D) is useful but doesn't directly address the high cardinality of 'state'. Label encoding (E) is not appropriate for nominal categorical features like 'state' as it introduces ordinality.


NEW QUESTION # 70
A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?

  • A. Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.
  • B. While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
  • C. Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
  • D. Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.
  • E. Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).

Answer: A,C,D,E

Explanation:
Option A is true because label encoding converts strings into integers, which are more memory-efficient than storing numerous one-hot encoded columns. Snowflake's columnar storage further optimizes integer storage. Option B is also true; label encoding inherently creates an ordinal relationship that might not be valid for nominal features like city names. Option C is incorrect; simple binarization (e.g., > threshold) of label encoded data doesn't remove the arbitrary ordinal relationship; more complex binarization techniques would be needed. Option D is accurate; binarization reduces dimensionality but sacrifices granularity, leading to information loss. Option E is correct because carefully chosen thresholds might correlate with the target variable and improve predictive power.


NEW QUESTION # 71
You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?

  • A. None of the above
  • B.
  • C.
  • D.
  • E.

Answer: B

Explanation:
Option A correctly addresses all requirements: Filters usage data for the last 30 days. Corrects negative values by setting them to 0 using and ' Calculates the sum of for each customer. Uses a 'LEFT JOIN' to include all customers, even those without recent usage data. Uses 'coalesce()' to set the to 0 for customers with no usage data after the join. Option B uses an ' INNER JOIN' , which would exclude customers without any recent usage data, violating the requirement to include all customers. Option C does not treat negative usage values correctly. Option D uses a "RIGHT JOIN' which would return incorrect results. Option E isn't right as option A correctly addresses all the scenarios.


NEW QUESTION # 72
You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

  • A. Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
  • B. Increase the regularization strength in your model.
  • C. Remove outlier data points to improve overall model performance.
  • D. Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
  • E. Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns

Answer: A,D

Explanation:
A sinusoidal pattern in the residuals indicates that the model is not adequately capturing the seasonal patterns in the data. Incorporating lagged features (Option B) allows the model to learn from past sales trends. A Box-Cox transformation (Option C) can help stabilize the variance and improve the model's fit. Increasing regularization (Option A) or removing outliers (Option D) might help in some cases, but they are not the primary solutions for a sinusoidal pattern. Linear regression models are unlikely to capture sinusoidal patterns, so Option E is wrong.


NEW QUESTION # 73
......

The top of the lists SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam practice questions features are free demo download facility, 1 year free updated Snowflake exam questions download facility, availability of SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) exam questions in three different formats, affordable price, discounted prices and Snowflake DSA-C03 exam passing money back guarantee.

DSA-C03 Reliable Test Voucher: https://www.vceprep.com/DSA-C03-latest-vce-prep.html

An Examination Score report (PDF) should be submitted to billing@VCEPrep DSA-C03 Reliable Test Voucher.com to claim the exam exchange, a refund will be provided, Snowflake Printable DSA-C03 PDF We did two things to realize that: hiring experts and researching questions of past years, For more details, please contact our customer service: sales@VCEPrep DSA-C03 Reliable Test Voucher.com Shipping VCEPrep DSA-C03 Reliable Test Voucher product(s) will be available for instant download after the successful payment, Snowflake Printable DSA-C03 PDF Your knowledge is broadened and your ability is enhanced, what an excellent thing.

Move back through the items on a web page, the Address bar, or the Links bar, Rules Instant DSA-C03 Discount about Data Access, An Examination Score report (PDF) should be submitted to billing@VCEPrep.com to claim the exam exchange, a refund will be provided.

100% Pass Snowflake - DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam Pass-Sure Printable PDF

We did two things to realize that: hiring experts New DSA-C03 Test Pdf and researching questions of past years, For more details, please contact our customer service: sales@VCEPrep.com Shipping VCEPrep DSA-C03 product(s) will be available for instant download after the successful payment.

Your knowledge is broadened and your ability is enhanced, Instant DSA-C03 Discount what an excellent thing,
We feel honored that you spare some time paying attention to DSA-C03 test questions, which we have carefully made as detailed as possible to ensure you to get desired DSA-C03 pass-king information.

Report this page