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Snowflake DAA-C01 Exam Syllabus Topics:

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

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                                        Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q28-Q33):

                                        NEW QUESTION # 28
                                        How do Snowsight's data loading capabilities impact data ingestion?

                                        Answer: B

                                        Explanation:
                                        Snowsight allows streamlined data import from various sources, enhancing data ingestion capabilities.


                                        NEW QUESTION # 29
                                        Configuring subscriptions and updates in a dashboard tool primarily helps in:

                                        Answer: C


                                        NEW QUESTION # 30
                                        You have a Snowflake table named 'CUSTOMER DATA' with a 'JSON DATA' column containing nested JSON objects. You need to extract specific fields from the nested JSON, transform the data, and load it into a new table named 'CLEANED CUSTOMERS'. You want to automate this process using a task and stream. Which of the following SQL statements, when combined correctly, provide the most efficient and reliable solution for automating this data transformation?

                                        Answer: C

                                        Explanation:
                                        The correct answer (D) uses a stream to capture changes in the 'CUSTOMER_DATX table. The task is chained to the stream using 'AFTER ensuring it runs only when there are changes in the stream. It then inserts the transformed data (name, age) from the stream into 'CLEANED_CUSTOMERS' , filtering based on Options A and C don't leverage streams and will re-process the entire 'CUSTOMER DATA table each time the task runs. Option B uses a MERGE statement without considering any audit columns from the stream and option E sets SHOW INITIAL ROWS = TRUE on stream that are deprecated and doesn't ensure task execution correctly because task doesn't use the stream state properly. A stream is crucial for incremental loading and efficient processing of only the changed data.


                                        NEW QUESTION # 31
                                        You are tasked with identifying PII (Personally Identifiable Information) within several tables in your Snowflake data warehouse before granting access to a new analytics team. You have a database called 'CUSTOMER DATA with tables 'CUSTOMERS' , 'ADDRESSES' , and 'ORDERS'. Which of the following SQL queries, leveraging Snowflake's information schema, would be the most efficient and least intrusive method to discover potential PII columns, assuming you have a naming convention where PII columns often contain terms like 'EMAIL', 'PHONE', 'SSN', or 'NAME'?

                                        Answer: D

                                        Explanation:
                                        The correct answer is E. It leverages the 'information_schema.columns' view, which is a standard and efficient way to retrieve metadata in Snowflake. using 'table_schema = 'CUSTOMER_DATA" and 'table_name IN ('CUSTOMERS', 'ADDRESSES', 'ORDERS')' filters the results to the specific database and tables of interest, making it more efficient than scanning all columns in the account or using table_catalog'. ensures case-insensitive matching. Options A and D uses either wrong schema or wrong like. Option B and C uses account_usage, that can introduce delay to data availability since that is for billing and monitoring and also does not filter by table names


                                        NEW QUESTION # 32
                                        You are working with a table 'ORDERS' containing order data, and a table 'CUSTOMER SEGMENTS containing customer segment information. The 'ORDERS' table has columns 'ORDER ID', 'CUSTOMER ID, and 'ORDER AMOUNT'. The 'CUSTOMER SEGMENTS' table has columns 'CUSTOMER ID', 'SEGMENT ID', and 'SEGMENT NAME'. You need to create a query that enriches the 'ORDERS' table with the customer segment information. However, a customer can belong to multiple segments. You want to include all segments a customer belongs to in the enriched data, resulting in potentially multiple rows per order if the customer is in multiple segments. The output should include 'ORDER ID, 'ORDER AMOUNT, 'SEGMENT ID', and 'SEGMENT NAMES. Which SQL statement would correctly enrich the ORDERS table without losing any order information, even if customers belong to multiple segments?

                                        Answer: A

                                        Explanation:
                                        An 'INNER JOIN' is the correct choice here. It will return all matching rows between the 'ORDERS' and tables based on the 'CUSTOMER ID. If a customer belongs to multiple segments, each segment will be returned in a separate row associated with the order. "LEFT JOIN' includes rows from the left table even without a match in the right table which is not needed. 'GROUP BY' in the INNER JOIN' will combine multiple segments with 'ORDER_IDS into one row, which is not needed as all segment names are expected. The subquery approach would only return one segment name per order. 'RIGHT JOIN' include all customer segments on the right which is also not needed.


                                        NEW QUESTION # 33
                                        ......

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