DAA-C01최신덤프공부자료 - DAA-C01응시자료

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최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q148-Q153):

질문 # 148
You are developing a Snowflake stored procedure that uses an external Python library (e.g., scikit-learn for machine learning). The library is not natively available within Snowflake's Python environment. What is the correct process to include and utilize this external library within your stored procedure?

A. Simply import the library in your Python code within the stored procedure. Snowflake automatically downloads and installs any missing libraries from PyPl when the procedure is executed.
B. Create a Snowflake stage, upload the library's '.whl' file to the stage, and then use the 'CREATE PROCEDURE statement with the 'IMPORTS' clause to specify the stage and .whl' file. Snowflake will then install the library during procedure creation.
C. Upload the library using the Snowflake web interface, so Snowflake will know which library it should be using.
D. Include the source code of the library directly within the stored procedure's Python code.
E. Use the 'pip install' command within the stored procedure's Python code to install the library from PyPl during each execution of the procedure.

정답：B

설명：
Option B is the correct approach. Snowflake uses stages and the 'IMPORTS' clause to manage external dependencies for Python stored procedures. You must upload the .whl' file of the library to a stage and then reference it in the 'CREATE PROCEDURE' statement. This ensures that the library is available when the procedure is executed. Option A is incorrect because Snowflake does not automatically download libraries from PyPl. Option C is incorrect because you cannot execute shell commands like 'pip install' within a stored procedure. Option D is generally impractical for larger libraries, and Option E isn't a valid approach.

질문 # 149
A company stores sensor data, including timestamps (ts), sensor ID (sensor_id), and readings (reading_value), in a Snowflake table named 'sensor_data'. Due to sensor malfunctions, some readings are significantly higher or lower than expected (outliers). Which of the following approaches are suitable in Snowflake to calculate the average reading value for each sensor, EXCLUDING readings that fall outside of two standard deviations from the mean for that sensor?

A. Using a HAVING clause after grouping by sensor_id to filter out groups where the range of reading_value exceeds a certain threshold.
B. Using window functions to calculate the mean and standard deviation for each sensor, then filtering the results to exclude outliers using a WHERE clause.
C. Using a QUALIFY clause with window functions to filter out the outlier readings based on their distance from the mean, prior to calculating the final average.
D. Using a LATERAL FLATTEN function to transform reading values into an array, calculate the mean and standard deviation in a JavaScript UDF, then use ARRAY SLICE to remove outliers before calculating the average.
E. Calculating the mean and standard deviation for each sensor in a subquery, then joining the results with the original data and filtering based on the calculated values.

정답：B,C,E

설명：
Options A, B and C provides valid ways to determine outliers. A is based on direct filtering based on standard deviation on the original table using window function. B uses Sub query approach and filtering. C allows to use QUALIFY clause with window functions for filtering before aggregation. D attempts to filter groups based on range which is not the intent of the original question to filter on a per reading basis if its an outlier or not. Option E, although technically possible, introduces significant complexity and performance overhead with the use of UDF and array manipulation for a task achievable with standard SQL.

질문 # 150
You are tasked with creating a dashboard to monitor the performance of different marketing channels (e.g., email, social media, paid advertising). The data includes daily spend, impressions, clicks, and conversions for each channel. Which approach would BEST allow you to visualize the return on investment (ROI) for each channel over time, identify channels with diminishing returns, and enable stakeholders to easily compare channel performance?

A. Develop an interactive dashboard in Looker Studio, utilizing calculated fields to derive ROI for each channel (e.g., conversions / spend). Use a combination of line charts, bar charts (ROI per channel), and scatter plots (spend vs. conversions) with trendlines. Implement drill-down capabilities to view daily performance metrics.
B. Create separate line charts for each channel showing spend, impressions, clicks, and conversions over time, using a static reporting tool like SSRS.
C. Create a static report in Tableau using only aggregate measures to calculate the total ROI for each channel and display it in a table.
D. Export the data to Excel and create a pivot table summarizing spend and conversions for each channel. Generate a simple bar chart showing total ROI for each channel.
E. Use Snowflake's built-in charting capabilities to create a series of pie charts showing the percentage of total spend allocated to each channel.

정답：A

설명：
Option B is the most suitable because it uses an interactive dashboard (Looker Studio) with calculated fields to derive ROI. The combination of line charts, bar charts, and scatter plots provides a comprehensive view of channel performance over time. Trendlines in the scatter plots help identify diminishing returns. Drill-down capabilities allow for detailed analysis. Option A creates separate charts, making comparison difficult. Option C is limited to summary data. Option D focuses on spend allocation, not ROI. Option E provides only a static view of total ROI.

질문 # 151
You have a large dataset in Snowflake containing customer order information stored in a table named 'ORDERS' with columns 'ORDER_ID' ONT), 'CUSTOMER_ID' ONT), 'ORDER_DATE (DATE), 'TOTAL_AMOUNT' (FLOAT), and 'DISCOUNT_APPLIED' (BOOLEAN). You need to use Snowsight dashboards to analyze customer spending behavior and identify potential outliers. Which of the following visualizations, combined with appropriate SQL queries, would be MOST effective in identifying customers with unusually high or low order values? (Select TWO)

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

정답：A,B

설명：
Options A and C are the most effective. A Box Plot (A) is ideal for identifying outliers in a distribution. By visualizing the distribution of total order amounts per customer, you can easily spot customers with unusually high or low spending. A Scatter Plot (C) directly shows the relationship between customer ID and total spending, making it easy to visually identify outliers based on their position relative to other data points. Option B is more suitable for trend analysis over time, and options D and E are useful but don't directly highlight individual customer outliers in terms of order value.

질문 # 152
You are investigating why a Snowflake data replication process between two regions is experiencing significant lag. You need to collect data to determine if the issue stems from network latency, insufficient warehouse resources in the target region, or data transformation bottlenecks. Select the data collection methods that will provide the MOST relevant insights.

A. Monitor the replication lag metrics (e.g., DATABASE REPLICATION_LAG, TABLE REPLICATION_LAG) exposed through Snowflake system functions and the web interface for both the source and target regions.
B. Restart the data replication process.
C. Analyze the query history in the target region to identify slow-running transformation queries that might be bottlenecking the replication process.
D. Monitor the CPU utilization of the virtual machines running the Snowflake service in both regions.
E. Run traceroute commands between the source and target regions to measure network latency.

정답：A,C,E

설명：
Options A, B, and C provide specific data points relevant to the identified potential causes. Monitoring replication lag metrics (A) directly quantifies the lag. Traceroute (B) measures network latency. Analyzing query history (C) identifies transformation bottlenecks. Restarting the process (D) might temporarily resolve the issue but doesn't address the root cause. Snowflake manages the underlying infrastructure; therefore, monitoring VM CPU utilization (E) is not something that a data analyst has access to or is needed for the diagnostic in this case. The Snowflake service runs and manages the queries.

질문 # 153
......

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음식 식는다구요, 고갤 푹 떨구고 있던 유나가 눈을 반짝이며 지욱을 바라보았다, 우리DumpTOP에서 제공되는 모든 덤프들은 모두 100%보장 도를 자랑하며 그리고 우리는 일년무료 업데이트를 제공합니다, DAA-C01 Dumps를 데려가 주시면 기적을 안겨드릴게요.

DAA-C01최신 덤프공부자료 덤프는 SnowPro Advanced: Data Analyst Certification Exam 시험문제의 모든 유형과 범위를 커버

IT업계엘리트한 강사들이 퍼펙트한 Snowflake DAA-C01 덤프문제집을 제작하여 디테일한 시험문제와 답으로 여러분이 아주 간단히Snowflake DAA-C01시험을 패스할 수 있도록 최선을 다하고 있습니다, 하지만 우리DumpTOP를 선택함으로Snowflake DAA-C01인증시험은 그렇게 어렵지 않다는 것을 알게 될 것입니다.

자격증을 많이 취득하면 좁은 취업문도 넓어집니다.

Amelia Adams Amelia Adams

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DAA-C01최신덤프공부자료 - DAA-C01응시자료

DAA-C01응시자료, DAA-C01덤프최신버전

최신 SnowPro Advanced DAA-C01 무료샘플문제 (Q148-Q153):

DAA-C01최신 덤프공부자료 덤프는 SnowPro Advanced: Data Analyst Certification Exam 시험문제의 모든 유형과 범위를 커버

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