最受歡迎的新版DAA-C01題庫，免費下載DAA-C01考試資料得到妳想要的Snowflake證書
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DAA-C01熱門認證，免費下載DAA-C01考題

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最新的 SnowPro Advanced DAA-C01 免費考試真題 (Q60-Q65):

問題 #60
In Snowsight, what is the significance of creating diverse chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?

答案：D

解題說明：
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.


問題 #61
When performing a descriptive analysis using Snowsight dashboards, how do they assist in summarizing large data sets?

答案：C

解題說明：
Snowsight dashboards provide visual representations that aid in quick comprehension of large data sets during summarization.


問題 #62
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)

答案：A,B,C

解題說明：
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.


問題 #63
What scheme is used by Snowflake to estimate the approximate similarity between two or more data sets?

答案：A

解題說明：
Snowflake provides several "approximate" functions designed to handle massive scale with high efficiency.
While HyperLogLog (HLL) is the standard for estimating cardinality (unique counts), and APPROX_TOP_K is used for frequency estimation, the specific task of determining the similarity between two sets relies on a different probabilistic algorithm.
The MINHASH function is Snowflake's implementation for estimating the Jaccard similarity coefficient between two or more sets. Jaccard similarity is defined as the size of the intersection divided by the size of the union of the sample sets. Calculating an exact Jaccard similarity on billions of rows would be computationally expensive. MINHASH solves this by creating a "signature"-a small, fixed-size binary representation of the data. By comparing these signatures rather than the raw data, Snowflake can efficiently estimate how similar the original datasets are.
Evaluating the Options:
* Option B (APPROX_PERCENTILE) is used to estimate the value at a specific percentile (e.g., the
95th percentile of latency).
* Option C (HyperLogLog) is used for estimating cardinality (the number of unique elements), not the similarity between sets.
* Option D (APPROX_TOP_K) identifies the most frequent elements in a dataset.
* Option A is the 100% correct answer. It is the specific function built into Snowflake for similarity estimation using the MinHash scheme.


問題 #64
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?

答案：E

解題說明：
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.


問題 #65
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