간결한 내용

DAA-C01 덤프문제는 다년간의 다양한 시험에 대한 분석을 바탕으로, 시험문제의 주요 발전 경향에 따라 응시자가 직면할 어려움을 정면 돌파하기 위하여 전문가들이 자신만의 경험과 끊임없는 노력으로 제작한 최고품질의 시험자료입니다.다른 교육 플랫폼과 달리 SnowPro Advanced: Data Analyst Certification Exam 시험덤프는 오래된 문제는 삭제하고 새로운 문제는 바로바로 추가하여 덤프가 항상 가장 최신버전이도록 간결하고 눈에 잘 띄는 텍스트로 요약되어 있기에 덤프만 완벽하게 마스터 하시면 DAA-C01 시험패스는 더는 어려운 일이 아닙니다.

우리의 SnowPro Advanced: Data Analyst Certification Exam 시험 덤프 문제는 최고품질의 시험대비 자료입니다. 전문가들이 최신 실러버스에 따라 몇년간의 노하우와 경험을 충분히 활용하여 연구제작해낸 자료라 해당 시험의 핵심문제를 모두 반영하고 있습니다.DAA-C01 덤프로 시험을 준비하시면 시험패스는 더는 어려운 일이 아닙니다. DAA-C01 시험에서 출제 가능성이 높은 문제만 정리한 최신 버전 자료라 가장 적은 문항수로 모든 응시자가 효율적인 시험공부를 할 수 있도록 하고 부담 없이 한번에 DAA-C01 시험을 즉시 통과할 수 있도록 도와드립니다.

데모문제 다운로드

커리큘럼 소개

대부분의 분들에게 있어서 자격증 시험이 처음일 수 있으므로 자격증 시험과 관련된 많은 정보는 복잡하고 난해할수 있습니다. 하지만 자격증 취득 초보자들의 덤프공부 후기에 따르면 DAA-C01 덤프는 시험의 모든 출제범위와 시험유형을 커버하고 있어 덤프에 있는 문제와 답만 기억하시면 SnowPro Advanced: Data Analyst Certification Exam 시험을 쉽게 패스하여 자격증을 취득할수 있다고 합니다. DAA-C01 시험대비 덤프는 초보자의 눈높이에 맞추어 덤프를 사용하시는 분께서 보다 편하게 공부할수 있도록 엘리트한 전문가들의 끊임없는 연구와 자신만의 노하우로 최선을 다한 자료입니다.덤프의 도움으로 여러분은 업계에서 또 한층 업그레이드 될것입니다.

진정한 시뮬레이션 환경

많은 응시자 분들이 처음 자격증 시험에 도전하는 것이라 시험보실 때 경험 부족으로 인해 시험시간에 너무 긴장하여 평소 실력을 발휘하지 못하는 경우가 있습니다.이를 피면할수 있도록 미리 SnowPro Advanced: Data Analyst Certification Exam 시험과 비슷한 환경에서 연습하는 훈련을 통해 실제 시험에서 긴장을 완화시키는 것이 좋습니다. 저희는DAA-C01 실제 시험 시뮬레이션 테스트 환경에 해당하는 제품을 가지고 있습니다. 제품 구매후 자신의 계정에 로그인하시고 실제 시험 환경을 체험해 보시면 시험 환경에 적응되어 DAA-C01 시험보실때 문제 푸는 방법을 모색하는 시간이 줄어들어 자신감이 생겨 한방에 시험패스 가능할것입니다.

최신 SnowPro Advanced DAA-C01 무료샘플문제:

1. You are tasked with enriching a customer dataset in Snowflake. The 'CUSTOMER DATA table contains customer IDs and country codes. You have a separate 'COUNTRY INFORMATION' table that contains country codes and corresponding currency codes. Both tables reside in the 'RAW DATA schema of the 'ANALYTICS DB' database. You need to create a view called ENRICHED CUSTOMER DATA' in the 'TRANSFORMED DATA' schema that joins these tables to add currency information to the customer data'. You want to optimize this view for performance. Which of the following approaches would be the MOST efficient and scalable, considering potential data volume increases?

A) Create a secure view joining the two tables and granting access to users.
B) Create a standard view using a JOIN between 'CUSTOMER DATA' and 'COUNTRY INFORMATION'. Refresh the view regularly using a scheduled task.
C) creates a materialized view with clustering enabled on the 'COUNTRY_CODE column after joining 'CUSTOMER_DATX and 'COUNTRY_INFORMATION'.
D) Create a materialized view using a simple JOIN between 'CUSTOMER_DATA' and 'COUNTRY_INFORMATION'.
E) Use a User-Defined Function (UDF) to look up the currency code from the "COUNTRY_INFORMATION' table based on the customer's country code.

2. You are tasked with creating a dashboard to monitor the performance of several marketing campaigns. The dashboard should allow users to quickly identify underperforming campaigns and drill down to understand the reasons. Which of the following combinations of visualizations and data interaction techniques would BEST achieve this goal? Assume that you are using Snowflake as your data source and connecting to a BI tool.

A) Abar chart comparing the revenue generated by each campaign, with a filter to select a specific date range. Implement cross-filtering such that selecting a bar filters other charts on the dashboard (e.g., demographic distribution, geographic distribution) to show data related to that campaign.
B) A scatter plot showing campaign cost vs. revenue, without any filters or drill-down capabilities.
C) A table showing the campaign name, total cost, and total revenue, sorted by revenue in descending order. No drill-down capabilities are included.
D) Apie chart showing the percentage of total revenue generated by each campaign.
E) A bullet chart comparing actual revenue to target revenue for each campaign, sorted by the difference between actual and target. Implement drill-down capabilities to view campaign-specific metrics (e.g., cost per acquisition, conversion rate) and associated customer data.

3. You are preparing a CSV file for ingestion into Snowflake, and you need to ensure that the data types are correctly interpreted. The CSV contains a column named 'transaction_amount' that sometimes contains values with leading zeros (e.g., '00123.45'). You want to load this data into a Snowflake table where 'transaction_amount' is defined as NUMBER(IO, 2). Without modifying the CSV file itself, how can you ensure that the leading zeros are handled correctly during the COPY INTO operation?

A) Define the 'transaction_amount' column as VARCHAR in Snowflake, load the data, and then cast it to NUMBER(10, 2) using a transformation query, which will implicitly remove leading zeros.
B) Use the 'VALIDATE option in COPY INTO to identify rows with leading zeros and manually correct them in the CSV file.
C) Snowflake automatically handles leading zeros in numeric fields during COPY INTO, so no special action is required.
D) During the COPY INTO operation, use the 'TRANSFORM_COLUMN' option to cast the VARCHAR column to a NUMBER(10, 2). Snowflake will implicitly handle the leading zeros during the cast.
E) Use the 'STRIP file format option to remove the leading zeros before loading.

4. You are tasked with enriching your company's customer transaction data with external economic indicators (e.g., unemployment rate, GDP) obtained from a Snowflake Marketplace data provider. The transaction data resides in a table 'TRANSACTIONS' with columns 'TRANSACTION (INT), 'TRANSACTION DATE (DATE), and (VARCHAR). The economic indicators data, obtained from the Marketplace, is available in a table 'ECONOMIC DATA' with columns 'DATE (DATE), ZIP_CODE (VARCHAR), 'UNEMPLOYMENT RATE (NUMBER), and 'GDP' (NUMBER). Due to data quality issues, some zip codes in both tables are missing or malformed. You need to create a view that efficiently joins these two tables, handles missing or malformed zip codes, and provides the transaction data enriched with the economic indicators. Which of the following approaches is the MOST robust and efficient way to create this enriched view, minimizing data loss and maximizing data quality?

A) Create a view using a 'LEFT OUTER JOIN' between 'TRANSACTIONS and ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION_DATE =ECONOMIC_DATA.DATE' and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE'. Additionally, use the function to handle malformed zip codes and the 'NVL' function to replace missing or malformed zip codes with a default zip code (e.g., '00000') for joining purposes. Also include a new column "ENRICHMENT SUCCESS' that flag indicates that the join was successful or whether data was enriched using the default zip code.
B) Create a stored procedure that iterates through each transaction in 'TRANSACTIONS' , attempts to find a matching economic data record in ECONOMIC_DATA' based on date and zip code, and updates a new 'TRANSACTIONS_ENRICHED table with the economic indicators. Handles missing zipcodes by setting the 'UNEMPLOYMENT RATE' and 'GDP ' to 0 for any record in transaction which zip code is missing.
C) Create a view that first filters out all rows with missing or malformed zip codes from both 'TRANSACTIONS' and 'ECONOMIC DATA' using 'WHERE clauses and regular expressions to validate the zip code format. Then, perform an ' INNER JOIN' between the filtered datasets on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE.
D) Create a Snowflake Task that runs daily to update a materialized view that joins 'TRANSACTIONS' and 'ECONOMIC_DATX on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC DATA.DATE-' and 'TRANSACTIONS.CUSTOMER ZIP = ECONOMIC DATA.ZIP CODE , handling missing zip codes by skipping those records entirely.
E) Create a view that performs a simple 'JOIN' between 'TRANSACTIONS' and 'ECONOMIC DATA' on 'TRANSACTIONS.TRANSACTION DATE = ECONOMIC_DATDATE and 'TRANSACTIONS.CUSTOMER_ZIP = ECONOMIC_DATA.ZIP_CODE. This approach ignores missing or malformed zip codes.

5. You have a Snowflake table 'EMPLOYEES' with columns 'EMPLOYEE ID' ONT, PRIMARY KEY), 'SALARY' and 'DEPARTMENT' (VARCHAR). You need to enforce the following business rules: 1. 'SALARY' must be a positive value. 2. 'DEPARTMENT' must be one of the following values: 'SALES', 'MARKETING', 'ENGINEERING'. 3. If the Employee is in 'SALES' Department, Salary should be between 50000 and 100000. Which of the following is the most appropriate and efficient approach using Snowflake constraints and features?

A) Use a CHECK constraint for 'SALARY > , a CHECK constraint 'DEPARTMENT IN ('SALES', 'MARKETING', 'ENGINEERING') , and a third CHECK constraint 'CASE WHEN DEPARTMENT = 'SALES' THEN SALARY BETWEEN 50000 AND 100000 ELSE TRUE END'.
B) Use CHECK constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.
C) Enforce rules using stored procedure at the time of insertion and updation.
D) Use CHECK constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
E) Use a CHECK constraint for 'SALARY > , an ENUM type for 'DEPARTMENT' , and a TRIGGER to enforce the salary range rule for the 'SALES' department.

질문과 대답: