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SnowPro Advanced - Data Analyst Practice Tests and PDFs

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SnowPro Advanced - Data Analyst

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Exam Code:

DAA-C01

Vendor Name:

Snowflake

Total Questions:

100

Last Updated Date:

April 7, 2025

Formate:

Practice Test Online

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Price:

$199.00

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    DAA-C01 Test Features


    Prepare with Confidence for the DAA-C01 – SnowPro Advanced - Data Analyst by Snowflake

    Boost your chances of success with our expertly crafted preparation materials for the DAA-C01 – SnowPro Advanced - Data Analyst, a key certification offered by Snowflake. Whether you're aiming to advance your career or validate your expertise, this exam is a crucial milestone in the  SnowPro Advanced Certification domain.

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    DAA-C01

    Practice makes perfect! Take this quiz now to test your knowledge and boost your confidence for the real exam.

    1 / 10

    A key aspect of performing exploratory ad-hoc analyses is: Response:

    2 / 10

    What are the hidden values (as indicated by the green circles) in the SQL query result grid? (Select TWO). Response: E. 1 for the hidden NTILE cell

    3 / 10

    What is a critical consideration when using data shares in Snowflake to join data with existing datasets? Response:

    4 / 10

    Which approach is most suitable for making data-driven predictions? Response:

    5 / 10

    A Data Analyst has been asked to produce a tile in a dashboard using Snowsight. The chart should always show orders for the last 30 days excluding partial days based on the order_date field. Which filter condition will meet this requirement? Response:

    6 / 10

    When planning for data volume collection, what is an important consideration to ensure scalability and performance? Response:

    7 / 10

    8 / 10

    The following JSON object is stored in a VARIANT column called src in a table called car_sales: {"vehicle" : [ {"make": "Honda", "model": "Civic", "year": "2019", "price": "20275", "extras":["ext warranty", "paint protection"]}, {"make": "Toyota", "model": "Camry", "year": "2021", "price": "28375", "extras":["ext warranty", "paint protection", "rust proofing"]} ]} Which query would return the following result? Response: src:vehicle.make::string AS make, src:vehicle.model::string AS model, src:vehicle.extras::string AS extras FROM car_sales ORDER BY make, model, extras; vm.value:make::string AS make, vm.value:model::string AS model, ve.value::string AS extras FROM car_sales ,lateral flatten(input => src:vehicle) AS vm ,lateral flatten(input => vm.value:extras) AS ve ORDER BY make, model, extras; vm.value:make::string AS make, vm.value:model::string AS model, vm.value:extras::string AS extras FROM car_sales ,lateral flatten(input => src:vehicle) AS vm ORDER BY make, model, extras; src:vehicle.make::string AS make, src:vehicle.model::string AS model, vm.value::string AS extras FROM car_sales ,lateral flatten(input => src:vehicle.extras) AS vm ORDER BY make, model, extras;

    9 / 10

    Which of the following is a key step in data preparation? Response:

    10 / 10

    Why is the Parquet format preferred for complex data sets? Response:

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