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Snowflake DEA-C01 Exam Syllabus Topics:

SectionObjectives
Topic 1: Semi-Structured and Advanced Data Handling- Advanced transformation techniques
  • 1. Complex transformations in SQL
    • 2. Flattening nested data
      - Semi-structured data processing
      • 1. JSON, Avro, Parquet handling
        • 2. VARIANT data type usage
          Topic 2: Performance Optimization- Query performance tuning
          • 1. Clustering and micro-partitions
            • 2. Caching mechanisms
              - Warehouse optimization
              • 1. Concurrency management
                • 2. Scaling strategies
                  Topic 3: Data Engineering Fundamentals on Snowflake- Data ingestion concepts
                  • 1. Continuous ingestion concepts
                    • 2. Batch loading strategies
                      - Snowflake architecture for data engineering
                      • 1. Virtual warehouses and compute scaling
                        • 2. Storage and compute separation
                          Topic 4: Security, Governance, and Data Sharing- Data governance
                          • 1. Secure data sharing concepts
                            • 2. Data masking policies
                              - Access control
                              • 1. Role-based access control (RBAC)
                                • 2. Object-level permissions
                                  Topic 5: Data Loading and Integration- Snowpipe and continuous ingestion
                                  • 1. Event-driven loading
                                    • 2. Automated ingestion workflows
                                      - Bulk data loading
                                      • 1. COPY INTO command usage
                                        • 2. File format handling
                                          Topic 6: Data Pipelines and Transformation- ETL/ELT design patterns in Snowflake
                                          • 1. Staging and transformation layers
                                            • 2. Data orchestration concepts
                                              - Streams and Tasks
                                              • 1. Automating pipelines with Tasks
                                                • 2. Change Data Capture (CDC) with Streams

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                                                  Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q328-Q333):

                                                  質問 # 328
                                                  UDTFs also called a table function, returns zero, one, or multiple rows for each input row?

                                                  正解：B

                                                  解説：
                                                  Explanation
                                                  UDFs may be scalar or tabular.
                                                  A scalar function returns one output row for each input row. The returned row consists of a single column/value.
                                                  A tabular function, also called a table function, returns zero, one, or multiple rows for each input row. A tabular UDF is defined by specifying a return clause that contains the TABLE keyword and specifies the names and data types of the columns in the table results. Tabular UDFs are often called UDTFs (user-defined table functions) or table UDFs.


                                                  質問 # 329
                                                  Snowflake does not provide which of following set of SQL functions to support retrieving infor-mation about tasks?

                                                  正解：A

                                                  解説：
                                                  Explanation
                                                  SYSTEM$CURRENT_USER_TASK_NAME
                                                  Returns the name of the task currently executing when invoked from the statement or stored proce-dure defined by the task.
                                                  SYSTEM$TASK_DEPENDENTS_ENABLE
                                                  Recursively resumes all dependent tasks tied to a specified root task.
                                                  TASK_DEPENDENTS
                                                  This table function returns the list of child tasks for a given root task in a DAG of tasks.
                                                  TASK_HISTORY
                                                  This table function can be used to query the history of task usage within a specified date range.


                                                  質問 # 330
                                                  Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)

                                                  正解：B、C

                                                  解説：
                                                  Explanation
                                                  The methods that will trigger an action that will evaluate a DataFrame are DataFrame.collect() and DataFrame.show(). These methods will force the execution of any pending transformations on the DataFrame and return or display the results. The other options are not methods that will evaluate a DataFrame. Option A, DataFrame.random_split(), is a method that will split a DataFrame into two or more DataFrames based on random weights. Option C, DataFrame.select(), is a method that will project a set of expressions on a DataFrame and return a new DataFrame. Option D, DataFrame.col(), is a method that will return a Column object based on a column name in a DataFrame.


                                                  質問 # 331
                                                  A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
                                                  Which conditions must be met? (Select THREE).

                                                  正解：B、E、F

                                                  解説：
                                                  Explanation
                                                  Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
                                                  The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
                                                  The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
                                                  The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
                                                  The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
                                                  The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
                                                  The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.


                                                  質問 # 332
                                                  A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
                                                  To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
                                                  Which solution will meet these requirements with the LEAST operational effort?

                                                  正解：A

                                                  解説：
                                                  Amazon S3 Storage Lens provides a comprehensive view of S3 storage usage and activity. It includes metrics such as the number of incomplete multipart uploads and the number of non- current object versions, making it an ideal tool for gathering insights into storage inefficiencies like outdated versions and incomplete uploads. This solution requires minimal operational effort as it is built into S3 and provides a user-friendly dashboard with detailed insights.
                                                  Using the AWS CLI to manually list and check multipart uploads and object versions would require scripting and regular execution, leading to higher operational overhead.
                                                  S3 Inventory provides reports on the objects in your bucket, but it does not directly provide information on incomplete multipart uploads or outdated versions. You would need to process the data further to gather the specific insights.
                                                  AWS usage reports are more focused on billing and usage rather than detailed storage optimization metrics like incomplete multipart uploads and outdated object versions.


                                                  質問 # 333
                                                  ......

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