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In data presentations for business use analyses, what significance do identifying patterns and trends hold?
A. Recognizing patterns and trends limits data exploration.
B. It complicates data analysis, hindering decision-making.
C. Patterns and trends don't impact business use analyses significantly.
D. Identifying patterns and trends aids in insightful analyses.
Answer: D
Explanation:
Identifying patterns and trends aids in insightful analyses in business use scenarios.
NEW QUESTION # 44
A data analyst is working with JSON data representing product reviews. The JSON structure is complex, containing nested arrays and objects. The analyst needs to extract all the reviewer names (reviewer _ name) who gave a rating greater than 4, along with the product ID (product_id) and the overall average rating of that specific product. The table 'RAW REVIEWS contains a single VARIANT column named holding the JSON data'. Choose the most efficient and correct Snowflake SQL query to achieve this. Assume the JSON structure is consistent across all rows.
A. Option A
B. Option B
C. Option C
D. Option E
E. Option D
Answer: C
Explanation:
Option C correctly uses LATERAL FLATTEN to unnest the 'reviews' array within the JSON. It accurately extracts 'reviewer_name' and 'rating' from the flattened array 'r'. Critically, it correctly retrieves directly from the 'raw_json' column as the product ID is not within the nested 'reviews' array, but at the root level. The average rating is correctly calculated using a window function partitioned by the Option A incorrectly references the product_id in the flatten table.Option B is inefficient and incorrect because it assumes only one review exists ([0]) and uses GET PATH, which is less performant than direct path access. Options D is also incorrect for average rating as raw_json:reviews[0].rating will pick only the first rating in JSON to derive avg_rating.
NEW QUESTION # 45
Which statistical method is commonly used in forecasting based on historical data?
A. Inferential statistics
B. Data normalization
C. Simple data aggregation
D. Regression analysis
Answer: D
Explanation:
Regression analysis is frequently employed for forecasting based on historical data, predicting future trends based on past patterns.
NEW QUESTION # 46
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?
A. They enable the execution of repetitive tasks, enhancing efficiency.
B. UDFs enhance query performance more effectively than stored procedures.
C. Stored procedures hinder customization in data operations.
D. Stored procedures allow limited data accessibility for improved security.
Answer: A
Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.
NEW QUESTION # 47
A data analyst is performing an exploratory analysis on sales data and observes a highly skewed distribution for the 'sales_amount' column. Which of the following data transformation techniques are appropriate to mitigate the impact of the skewness and make the data more suitable for modeling and analysis? (Select all that apply)
A. Standardization
B. Winsorization
C. Log Transformation
D. Min-Max Scaling
E. Box-Cox Transformation
Answer: B,C,E
Explanation:
Log transformation and Box-Cox transformation are common techniques used to reduce skewness in data. Winsorization helps to reduce the impact of outliers, which can contribute to skewness. Standardization and Min-Max scaling are primarily used for feature scaling and do not directly address skewness.
NEW QUESTION # 48
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