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[Hardware] Dump DAA-C01 Check, DAA-C01 Practice Exam Online

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【Hardware】 Dump DAA-C01 Check, DAA-C01 Practice Exam Online

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q43-Q48):NEW QUESTION # 43
When performing forecasting, which factors are essential for accurate predictions? (Select all that apply)
  • A. Using basic arithmetic functions exclusively for forecasting
  • B. Examining trends and anomalies in historical data
  • C. Incorporating statistical methods for prediction accuracy
  • D. Relying solely on historical data without considering external influences
Answer: B,C
Explanation:
Incorporating statistical methods and considering trends/anomalies are crucial for accurate predictions in forecasting.

NEW QUESTION # 44
A retail company is analyzing sales data to optimize product placement and promotional campaigns. They have sales figures, customer demographics, and promotional campaign details stored in Snowflake. Which visualization technique and Snowflake feature combination would BEST help them identify nuanced correlations between customer age, product category, and the success rate of different promotional campaigns, allowing for interactive exploration and drill-down capabilities?
  • A. Create a static bar chart in Tableau showing average sales per age group for each product category. Use Snowflake's aggregate functions to calculate averages.
  • B. Develop an interactive dashboard using Streamlit connected to Snowflake, implementing cross-filtering between visualizations of customer age distribution, product category sales, and promotional campaign ROI. Use stored procedures in Snowflake to pre-calculate aggregated data and improve dashboard performance. Utilize heatmap to find correlation between Customer Age and Sales for promotion campaigns
  • C. Utilize Snowflake's Data Marketplace to access pre-built dashboards focusing on retail analytics, adapting them to visualize the company's data without further customization.
  • D. Export the data to a local CSV file and use Python's Pandas library to generate scatter plots showing the relationship between age, product category, and promotional campaign success. Create simple Histogram with no drilldown capabilities
  • E. Use Snowflake's built-in SQL to generate a correlation matrix between customer age and sales, and present it as a table in a static report. Implement UDF to calculate more customized Correlation coefficeint like Kendall+s Tau if data has non-normal distribution.
Answer: B
Explanation:
Option B is the most appropriate because it leverages an interactive dashboard (Streamlit) connected to Snowflake, enabling cross- filtering and drill-down capabilities. Using stored procedures in Snowflake to pre-calculate aggregated data enhances dashboard performance. Option A provides a static view, lacking interactivity. Option C relies on a static table, which is not ideal for exploratory analysis. Option D involves exporting data outside Snowflake, which is inefficient and less secure. Option E might not provide the specific visualizations or level of customization needed for detailed correlation analysis. The use of the heatmap allows a clearer correlation view.

NEW QUESTION # 45
When using Snowflake functions, how do scalar functions differ from aggregate functions?
  • A. Aggregate functions modify table structures.
  • B. Aggregate functions return single values.
  • C. Scalar functions work on groups of rows.
  • D. Scalar functions operate on entire tables.
Answer: B
Explanation:
Aggregate functions return single values from groups of rows, distinguishing them from scalar functions.

NEW QUESTION # 46
How does leveraging partition pruning enhance query performance in Snowflake?
  • A. Optimizes query planning by eliminating unnecessary partitions
  • B. Reduces metadata storage requirements
  • C. Speeds up data loading processes significantly
  • D. Limits data access for specific user roles
Answer: A
Explanation:
Partition pruning optimizes query planning by excluding unnecessary partitions from query execution, improving query performance by focusing on relevant data subsets.

NEW QUESTION # 47
Which of the following statements are true regarding the use of user-defined functions (UDFs) in Snowflake to optimize query performance, especially when compared to equivalent SQL expressions? (Select all that apply)
  • A. External functions and UDFs are the same, both reside outside snowflake.
  • B. Snowflake UDFs, especially those written in Java or Python, can introduce performance overhead due to the cost of invoking the external runtime environment.
  • C. UDFs can sometimes improve code readability by encapsulating complex logic, but this rarely translates into significant performance gains.
  • D. UDFs can be useful when the same complex calculation needs to be performed multiple times within a single query, potentially reducing code duplication and improving maintainability, thereby indirectly improving performance by simplification.
  • E. SQL UDFs generally perform better than Java or Python UDFs because they are executed natively within the Snowflake engine.
Answer: B,C,D,E
Explanation:
UDFs can improve code readability but don't always translate to performance gains. Java and Python UDFs incur overhead because they run outside the Snowflake engine. SQL UDFs are generally faster. They are useful for code reuse. External functions and UDFs are not the same: UDFs reside inside snowflake.

NEW QUESTION # 48
......
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