QUIZ 2025 USEFUL SNOWFLAKE SAMPLE DSA-C03 QUESTIONS

Quiz 2025 Useful Snowflake Sample DSA-C03 Questions

Quiz 2025 Useful Snowflake Sample DSA-C03 Questions

Blog Article

Tags: Sample DSA-C03 Questions, DSA-C03 Latest Exam Camp, Reliable DSA-C03 Study Notes, Free DSA-C03 Pdf Guide, DSA-C03 Test Question

Our DSA-C03 exam materials have plenty of advantages. For example, in order to meet the needs of different groups of people, we provide customers with three different versions of DSA-C03 actual exam, which contain the same questions and answers. They are the versions of the PDF, Software and APP online. You can choose the one which is your best suit of our DSA-C03 Study Materials according to your study habits.

If you choose to sign up to participate in Snowflake certification DSA-C03 exams, you should choose a good learning material or training course to prepare for the examination right now. Because Snowflake Certification DSA-C03 Exam is difficult to pass. If you want to pass the exam, you must have a good preparation for the exam.

>> Sample DSA-C03 Questions <<

The Snowflake DSA-C03 Exam Prep Material is Provided to

If you are looking to advance in the fast-paced and technological world, ExamCost is here to help you achieve this aim. ExamCost provides you with the excellent SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) practice exam, which will make your dream come true of passing the SnowPro Advanced: Data Scientist Certification Exam (DSA-C03) certification exam on the first attempt.

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q147-Q152):

NEW QUESTION # 147
You are working on a fraud detection model and need to prepare transaction data'. You have two tables: 'transactions' (transaction_id, customer_id, transaction_date, amount, merchant_id) and (merchant_id, city, state). You need to perform the following data cleaning and feature engineering steps using Snowpark: 1. Remove duplicate transactions based on 'transaction_id'. 2.
Join the 'transactions' table with the 'merchant_locations table to add city and state information to each transaction. 3. Create a new feature called 'amount_category' based on the transaction amount, categorized as 'Low', 'Medium', or 'High'. 4. The categorization thresholds are defined as follows: 'LoW: amount < 50 'Medium': 50 amount < 200 'High': amount >= 200 Which of the following statements about performing these operations using Snowpark are accurate?

  • A. The construct in Snowpark can be used to create the 'amount_category' feature directly within the DataFrame transformation without needing a UDF
  • B. Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories requires use of a User-Defined Function (UDF) as the logic can't be efficiently embedded in a single 'when' clause.
  • C. A LEFT JOIN should be used to join the 'transactions' and 'merchant_location' tables to ensure that all transactions are included, even if some merchant IDs are not present in the 'merchant_location' table.
  • D. Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories can be completed using the 'when' clause with multiple 'otherwise' clauses.
  • E. You can register SQL UDF to calculate the 'amount_category' using 'CASE WHEN' statement

Answer: A,D,E

Explanation:
Options C, D and E are correct. Option C is correct because Snowpark's construct allows creating new features based on conditional logic directly within DataFrame transformations, avoiding the need for a UDF for simple categorizations like this. Option D is correct because SQL UDF can be used to create a function that returns Option E is also correct because the method efficiently removes duplicates, and the 'when' clauses enables easy categorization of in snowflake. Option A is incorrect, the categorization doesn't necessarily require UDF. Option B is incorrect since a RIGHT or INNER join is valid as well.


NEW QUESTION # 148
A retail company, 'GlobalMart,' wants to optimize its product placement strategy in its physical stores. They have transactional data stored in Snowflake, capturing which items are purchased together in the same transaction. They aim to use association rule mining to identify frequently co-occurring items. Given the following simplified transactional data in a Snowflake table named 'SALES TRANSACTIONS:

Which of the following SQL-based approaches, combined with Snowpark Python for association rule generation (using a library like 'mlxtend'), would be the MOST efficient and scalable way to prepare this data for association rule mining, specifically focusing on converting it into a transaction-item matrix suitable for algorithms like Apriori? Assume 'spark' is a 'snowpark.Session' object connected to your Snowflake environment.

  • A. First extracting all the data from snowflake into pandas dataframe and then use pivoting and other pandas operations to convert to the needed format.
  • B. Employing a custom UDF (User-Defined Function) written in Java or Scala that directly processes the transactional data within Snowflake and outputs the transaction-item matrix in a format suitable for Snowpark. This offloads processing to compiled code within Snowflake, maximizing performance.
  • C. Creating a temporary table in Snowflake using a SQL query that aggregates items by transaction and represents them in a format suitable for Snowpark's 'mlxtend' library. Then load this temporary table into a Snowpark DataFrame and use it as input to the Apriori algorithm.
  • D. Utilizing Snowflake's SQL function within a stored procedure to concatenate items purchased in each transaction into a string, then processing the string using Python in Snowpark to create the transaction-item matrix. This approach minimizes data transfer but introduces string parsing overhead in Python.
  • E. Using Snowpark's 'DataFrame.groupBy(V and functions to aggregate items by transaction ID, then pivoting the data using to create the transaction-item matrix. This approach requires loading all data into the Snowpark DataFrame before pivoting.

Answer: E

Explanation:
Option A is the most efficient and scalable approach because Snowpark DataFrames are designed to handle large datasets efficiently within the Snowflake environment. Using 'groupBy(V, "agg()", and 'pivot()' allows Snowflake's engine to perform the data transformation in parallel and at scale. While option B avoids loading all the data, the string parsing in Python introduces overhead and potential scalability issues. Option C, while potentially performant, adds complexity to the solution. Option D can be a viable interim step, but performing the pivoting and aggregation directly within the Snowpark DataFrame is generally more streamlined. Option E is not efficient because it loads the data into pandas which is not scalable for big datasets.


NEW QUESTION # 149
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 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.
  • B. Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
  • C. 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.
  • 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,C

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 # 150
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 E
  • B. Option A
  • C. Option C
  • D. Option B
  • 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 # 151
You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?

  • A. Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.
  • B. Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.
  • C. Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.
  • D. Hardcode a role with 'ACCOUNTADMIN' privileges in your Jupyter Notebook using username and password.
  • E. Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.

Answer: E

Explanation:
Option D is the most secure. Key pair authentication is more secure than username/password. Specifying a dedicated virtual warehouse ensures dedicated compute. Option A is highly insecure. Option B doesn't directly create a Snowpark session. Option C, while using OAuth, requires proper setup and key pair provides more control. Option E is highly insecure and grants excessive privileges.


NEW QUESTION # 152
......

Are you racking your brains for a method how to pass Snowflake DSA-C03 exam? Snowflake DSA-C03 certification test is one of the valuable certification in modern IT certification. Within the last few decades, IT got a lot of publicity and it has been a necessary and desirable part of modern life. Snowflake certification has been well recognized by international community. So, most IT people want to improve their knowledge and their skills by Snowflake certification exam. DSA-C03 test is one of the most important exams and the certificate will bring you benefits.

DSA-C03 Latest Exam Camp: https://www.examcost.com/DSA-C03-practice-exam.html

Snowflake Sample DSA-C03 Questions Firstly, being the incomparably qualities of them, Our DSA-C03 test questions are available in three versions, including PDF versions, PC versions, and APP online versions, Our performance appraisal for the staff is the quality of DSA-C03 exam torrent materials and passing rate & satisfaction rate of users, Our DSA-C03 exam questions are followed by many peers many years but never surpassed.

Find the right ways to work with and motivate employees and colleagues DSA-C03 and avoid the wrong ways, When considering this method, remember that the hair that really gets noticed by the viewer is the hair at the edges.

New Sample DSA-C03 Questions & 100% Pass-Rate DSA-C03 Latest Exam Camp & Verified Snowflake SnowPro Advanced: Data Scientist Certification Exam

Firstly, being the incomparably qualities of them, Our DSA-C03 Test Questions are available in three versions, including PDF versions, PC versions, and APP online versions.

Our performance appraisal for the staff is the quality of DSA-C03 exam torrent materials and passing rate & satisfaction rate of users, Our DSA-C03 exam questions are followed by many peers many years but never surpassed.

Don't need a lot of time and money, only 30 hours of special training, and you can easily pass your first time to attend Snowflake certification DSA-C03 exam.

Report this page