Snowflake DAA-C01 dumps - in .pdf

DAA-C01 pdf
  • Exam Code: DAA-C01
  • Exam Name: SnowPro Advanced: Data Analyst Certification Exam
  • Updated: Aug 27, 2026
  • Q & A: 67 Questions and Answers
  • PDF Price: $59.99

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  • Exam Code: DAA-C01
  • Exam Name: SnowPro Advanced: Data Analyst Certification Exam
  • Updated: Aug 27, 2026
  • Q & A: 67 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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Snowflake DAA-C01 dumps - Testing Engine

DAA-C01 Testing Engine
  • Exam Code: DAA-C01
  • Exam Name: SnowPro Advanced: Data Analyst Certification Exam
  • Updated: Aug 27, 2026
  • Q & A: 67 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionObjectives
Topic 1: Security, Governance, and Data Sharing- Access control and security
  • 1. Role-based access control (RBAC)
    • 2. Authentication and encryption concepts
      - Data sharing and governance
      • 1. Secure data sharing
        • 2. Data masking and policies
          Topic 2: Data Transformation and Analysis- SQL-based transformations
          • 1. Semi-structured data (VARIANT, JSON, XML)
            • 2. Joins, aggregations, window functions
              - Analytical workloads
              • 1. Query optimization for analytics
                • 2. Materialized views and caching
                  Topic 3: Data Loading and Unloading- Data ingestion methods
                  • 1. Continuous ingestion and Snowpipe concepts
                    • 2. COPY INTO and bulk loading
                      - Data export
                      • 1. UNLOAD and external stages
                        Topic 4: Snowflake Architecture and Data Platform Fundamentals- Data platform fundamentals
                        • 1. Separation of storage and compute
                          • 2. Data lifecycle in Snowflake
                            - Snowflake architecture concepts
                            • 1. Cloud services layer, compute layer, storage layer
                              • 2. Virtual warehouses and scaling
                                Topic 5: Data Modeling and Performance Optimization- Performance tuning
                                • 1. Clustering and pruning techniques
                                  • 2. Warehouse sizing and auto-suspend/auto-resume
                                    - Modeling approaches in Snowflake
                                    • 1. Data normalization vs denormalization
                                      • 2. Star and snowflake schemas

                                        Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

                                        1. A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?

                                        A) Full outer join
                                        B) Cross join
                                        C) Inner join
                                        D) Left outer join


                                        2. What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?

                                        A) SYSTEM$ESTIMATE
                                        B) Count(Distinct *)
                                        C) HLL(*)
                                        D) Count(Distinct *)/Count(*)


                                        3. A Data Analyst for a ride-sharing company needs to assess the relationship between the number of active drivers in a city, and the average waiting time for passengers. Which query will determine if an increase in the number of active drivers is associated with a decrease in the average waiting time?

                                        A) SELECT CITY, SUM(ACTIVE_DRIVERS), VARIANCE(AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
                                        B) SELECT CITY, VARIANCE(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM
                                        RIDE_DATA GROUP BY CITY;
                                        C) SELECT CITY, SUM(ACTIVE_DRIVERS), AVG(AVERAGE_WAITING_TIME) FROM
                                        RIDE_DATA GROUP BY CITY;
                                        D) SELECT CITY, CORR(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;


                                        4. A Data Analyst has a very large table with columns that contain country and city names. Which query will provide a very quick estimate of the total number of different values of these two columns?

                                        A) SELECT COUNT(country, city) FROM TABLE1;
                                        B) SELECT DISTINCT COUNT(country, city) FROM TABLE1;
                                        C) SELECT HLL(country, city) FROM TABLE1;
                                        D) SELECT COUNT(DISTINCT country, city) FROM TABLE1;


                                        5. A Data Analyst is working with a table that has 1 record per day, with sales information. Which window function would calculate a 7-day moving average of sales, where SALES_DATE represents the date column?

                                        A) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
                                        CURRENT ROW)
                                        B) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
                                        CURRENT ROW)
                                        C) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
                                        CURRENT ROW)
                                        D) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
                                        CURRENT ROW)


                                        Solutions:

                                        Question # 1
                                        Answer: A
                                        Question # 2
                                        Answer: C
                                        Question # 3
                                        Answer: D
                                        Question # 4
                                        Answer: C
                                        Question # 5
                                        Answer: A

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