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Snowflake DSA-C02 Dumps

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SnowPro Advanced: Data Scientist Certification Exam
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The Snowflake DSA-C02 exam includes multiple-choice and multiple-select questions. It assesses candidates' knowledge of Snowflake architecture, data warehousing, performance optimization, and security, providing a comprehensive evaluation of their skills in implementing and managing Snowflake solutions.

The Snowflake DSA-C02 exam consists of 60 questions. These questions evaluate a candidate's proficiency in Snowflake's data platform, covering topics like architecture, data loading, performance tuning, and security, ensuring a thorough assessment of their expertise and practical knowledge.

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The Snowflake DSA-C02 exam duration is 115 minutes. This time frame allows candidates to thoroughly address 60 questions, testing their comprehensive understanding of Snowflake architecture, data warehousing concepts, performance tuning, and security practices in a practical context.

The Snowflake DSA-C02 exam focuses specifically on Snowflake's cloud data platform, covering architecture, data warehousing, and performance tuning. In contrast, Cisco's data certifications encompass broader networking, data center technologies, and data management skills across various platforms and infrastructures.

The Snowflake DSA-C02 certification benefits job roles such as Data Engineer, Data Architect, Database Administrator, and Business Intelligence Analyst. It validates expertise in Snowflake's data platform, enhancing capabilities in data warehousing, performance optimization, and secure data management.

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SnowPro Advanced: Data Scientist Certification Exam Questions and Answers

Questions 1

Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. which of the following Data transformation SQL function he can used to achieve the same?



He could not apply Transformation on Stream table data.


lateral flatten()





Questions 2

Which one is not the types of Feature Engineering Transformation?










Questions 3

Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10']. What does the aggregate method shown in below code do?

g = df.groupby(df.index.str.len())

g.aggregate({'A':len, 'B':np.sum})



Computes Sum of column A values


Computes length of column A


Computes length of column A and Sum of Column B values of each group


Computes length of column A and Sum of Column B values