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DAA-C01인증시험인기덤프, DAA-C01인증덤프샘플문제
그리고 ITDumpsKR DAA-C01 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1GDxvLCBBPd0e3hqt3PAZUWtvWlXm1JVY
Snowflake DAA-C01시험을 어떻게 패스할가 고민그만하시고 ITDumpsKR의Snowflake DAA-C01시험대비덤프를 데려가 주세요. 가격이 착한데 비해 너무나 훌륭한 덤프품질과 높은 적중율은 ITDumpsKR가 아닌 다른곳에서 찾아볼수 없는 혜택입니다. Snowflake DAA-C01 덤프구매전 데모부터 다운받아 공부해보세요.
Snowflake DAA-C01인증시험을 패스하고 자격증 취득으로 하여 여러분의 인생은 많은 인생역전이 이루어질 것입니다. 회사, 생활에서는 물론 많은 업그레이드가 있을 것입니다. 하지만DAA-C01시험은Snowflake인증의 아주 중요한 시험으로서DAA-C01시험패스는 쉬운 것도 아닙니다.
시험준비에 가장 좋은 DAA-C01인증시험 인기덤프 최신버전 덤프샘풀문제 다운
ITDumpsKR의Snowflake인증 DAA-C01덤프를 공부하시면 한방에 시험을 패스하는건 문제가 아닙니다. ITDumpsKR의Snowflake인증 DAA-C01덤프는 시험적중율 최고의 인지도를 넓히 알리고 있습니다.저희가 제공한 시험예상문제로 시험에 도전해보지 않으실래요? Snowflake인증 DAA-C01덤프를 선택하시면 성공의 지름길이 눈앞에 다가옵니다.
최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q41-Q46):
질문 # 41
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?
- A.
- B.
- C.
- D.
- E.
정답:E
설명:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.
질문 # 42
A financial institution needs to build a dashboard to monitor fraudulent transactions. They have transaction data (amount, timestamp, location, merchant category), customer data (age, income, credit score), and fraud flags. They want to identify patterns and correlations that indicate potentially fraudulent activity. Which of the following visualizations and Snowflake features, when used in combination, would be MOST effective for this purpose? (Select TWO)
- A. Develop an interactive dashboard using Power BI connected to Snowflake, incorporating a geographical map showing transaction locations, a time series chart of transaction volume, and a scatter plot of transaction amount vs. customer credit score. Use anomaly detection algorithms in Snowflake (via Snowpark or stored procedures using Python) to highlight unusual transactions. Implement User defined table Function in Power BI to generate more data.
- B. Use Snowflake's secure data sharing to share the raw transaction data with a third-party analytics vendor.
- C. Utilize a heatmap visualization in a Tableau dashboard to show the correlation between merchant category, transaction amount, and fraud flag. Implement interactive filters for customer age, income, and location. Implement Snowflake Row Access Policy to restrict data to specific users.
- D. Export the transaction data to a data lake and use Spark to perform fraud detection.
- E. Create a static report showing the total number of fraudulent transactions per month, using Snowflake's aggregate functions.
정답:A,C
설명:
Options B and D are the most effective. Option B utilizes an interactive dashboard with Power BI, incorporating geographical, temporal, and scatter plot visualizations to identify patterns. Anomaly detection algorithms in Snowflake further enhance the detection capabilities. Option D uses a heatmap to show correlations between key variables and implements interactive filters for detailed exploration. Implementing Row Access Policy would control data access, adding a security layer on top of sensitive financial data. Option A is not related to visualization or identifying correlations. Option C provides only a high-level summary. Option E involves exporting data outside Snowflake, which is less efficient and potentially less secure than using Snowflake's built-in capabilities.
질문 # 43
When implementing pre-math calculations (e.g., randomization, ranking, grouping) in Snowflake, how do they contribute to data analysis?
- A. Pre-math calculations affect only specific data types
- B. Ranking aids in data summarization and ordering
- C. Grouping ensures data integrity and consistency
- D. Randomization techniques introduce biases in data analysis
정답:B
설명:
Pre-math calculations like ranking assist in summarizing and ordering data, enabling meaningful insights and comparisons in data analysis.
질문 # 44
You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?
- A. Utilize Snowflake's caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.
- B. Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.
- C. Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.
- D. Use Snowflake's dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.
- E. Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.
정답:A
설명:
Using materialized views pre-computes aggregates and leverages Snowflake's caching. Dashboard-level filtering (option C) allows for dynamic filtering without querying the base data directly, providing the best performance and scalability. Options A and D are less ideal for real-time filtering; B is an anti-pattern in Snowflake, and E moves data out of Snowflake unnecessarily.
질문 # 45
You are analyzing website traffic data in Snowflake using Snowsight. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE), 'PAGE URL' (VARCHAR), 'VISITOR_ID (VARCHAR), and (INT - in seconds). You need to create a Snowsight dashboard to answer the following questions: 1. What is the average session duration per page URL? 2. What is the distribution of session durations (histogram)? 3. How many unique visitors are there per day? Which of the following approaches is the MOST efficient and appropriate for creating this Snowsight dashboard?
- A. Use the Snowsight Notebook feature with Python to load the data into pandas DataFrames. Then, calculate the required aggregations (average session duration, histogram bins, unique visitor counts) using pandas. Finally, create visualization tiles in Snowsight referencing the pandas DataFrames.
- B. Create three views on top of the WEB TRAFFIC table. Each view will calculate one of the required metrics. Create three tiles in Snowsight, each querying the corresponding view.
- C. Create three separate tiles in Snowsight, each using a direct SQL query. For the histogram, use the 'WIDTH BUCKET function within the SQL query to categorize session durations into buckets and then visualize the counts.
- D. Create a single tile with three tabs. Each tab displays one of the required visualizations (average session duration, histogram, unique visitors). Use the same base SQL query for all three tabs, filtering and aggregating the data differently for each tab.
- E. Create a single tile in Snowsight using a combination chart. Use the average session duration as the primary series, the histogram as a secondary series plotted on a different axis, and the number of unique visitors as a data label on each data point.
정답:C
설명:
Option A is the most efficient and appropriate. Snowsight's direct SQL query functionality, coupled with the 'WIDTH_BUCKET' function, provides a straightforward and performant way to create the required visualizations. Using views (Option E) adds an unnecessary layer of abstraction. Trying to combine all visualizations into a single tile (Options B and D) is not suitable for the different visualization types. Using Snowsight Notebook (Option C) introduces unnecessary overhead for this relatively simple descriptive analysis task. While Notebooks are powerful, they're best used for more complex data transformations and analysis that SQL cannot easily handle.
질문 # 46
......
Snowflake DAA-C01인증시험은 전업적지식이 강한 인증입니다. IT업계에서 일자리를 찾고 계시다면 많은 회사에서는Snowflake DAA-C01있는지 없는지에 알고 싶어합니다. 만약Snowflake DAA-C01자격증이 있으시다면 여러분은 당연히 경쟁력향상입니다.
DAA-C01인증덤프 샘플문제: https://www.itdumpskr.com/DAA-C01-exam.html
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그렇게 한 식경이 흘렀다, 대신 언제 그녀가 상단 물건을 훔치나 눈에DAA-C01불을 켜고 감시할 거다, 덤프에 있는 문제만 열심히 공부하시면 시험통과 가능하기에 시간도 절약해줄수있어 최고의 믿음과 인기를 받아왔습니다.
DAA-C01 인기시험덤프, DAA-C01 덤프, DAA-C01시험대비덤프
ITDumpsKR는 많은 IT인사들의 요구를 만족시켜드릴 수 있는 사이트입니다, ITDumpsKR는DAA-C01시험문제가 변경되면DAA-C01덤프업데이트를 시도합니다, ITDumpsKR 표 Snowflake인증DAA-C01덤프는 시험출제 예상문제를 정리해둔 실제시험문제에 가장 가까운 시험준비공부자료로서 공을 들이지않고도 시험패스가 가능합니다.
때문에 IT자격증이 많은 인기를 누리고 있습니다.이런 살아가기 힘든 사회에서 이런 자격증들 또한 취득하기가 넘 어렵습니다.Snowflake DAA-C01인증시험 또한 아주 어려운 시험입니다.
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