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Snowflake DEA-C01 Exam Syllabus Topics:

SectionObjectives
Data Pipelines and Transformation- Streams and Tasks
  • 1. Change Data Capture (CDC) with Streams
    • 2. Automating pipelines with Tasks
      - ETL/ELT design patterns in Snowflake
      • 1. Data orchestration concepts
        • 2. Staging and transformation layers
          Security, Governance, and Data Sharing- Data governance
          • 1. Data masking policies
            • 2. Secure data sharing concepts
              - Access control
              • 1. Object-level permissions
                • 2. Role-based access control (RBAC)
                  Performance Optimization- Warehouse optimization
                  • 1. Concurrency management
                    • 2. Scaling strategies
                      - Query performance tuning
                      • 1. Caching mechanisms
                        • 2. Clustering and micro-partitions
                          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
                                  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
                                          Semi-Structured and Advanced Data Handling- Semi-structured data processing
                                          • 1. JSON, Avro, Parquet handling
                                            • 2. VARIANT data type usage
                                              - Advanced transformation techniques
                                              • 1. Complex transformations in SQL
                                                • 2. Flattening nested data

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                                                  Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q13-Q18):

                                                  NEW QUESTION # 13
                                                  Which are the Cloud Platforms that Support Calling an External Function?

                                                  Answer: A


                                                  NEW QUESTION # 14
                                                  Data engineer designed the data pipelines using Snowpipe to load data files into Snowflake tables, what will happen in case few files with same name but modified data are queued for reloading?

                                                  Answer: D

                                                  Explanation:
                                                  Explanation
                                                  Snowflake uses file loading metadata to prevent reloading the same files (and duplicating data) in a table.
                                                  Snowpipe prevents loading files with the same name even if they were later modified (i.e. have a different eTag).
                                                  The file loading metadata is associated with the pipe object rather than the table. As a result:
                                                  Staged files with the same name as files that were already loaded are ignored, even if they have been modified, e.g. if new rows were added or errors in the file were corrected.
                                                  Truncating the table using the TRUNCATE TABLE command does not delete the Snowpipe file loading metadata.


                                                  NEW QUESTION # 15
                                                  Streams cannot be created to query change data on which of the following objects? [Select All that Apply]

                                                  Answer: E

                                                  Explanation:
                                                  Explanation
                                                  Streams supports all the listed objects except Query Log tables.


                                                  NEW QUESTION # 16
                                                  Snowflake does not treat the inner transaction as nested; instead, the inner transaction is a separate transaction.
                                                  What is term used to call these Transaction?

                                                  Answer: D


                                                  NEW QUESTION # 17
                                                  A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
                                                  The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.
                                                  Which combination of steps will meet these requirements with the LEAST development effort?
                                                  (Choose two.)

                                                  Answer: D,E

                                                  Explanation:
                                                  FluentBit is a lightweight, efficient tool to collect, process, and forward logs from the EKS cluster.
                                                  It integrates well with AWS services like CloudWatch or OpenSearch for storing and analyzing logs. OpenTelemetry is an open-source standard for collecting distributed traces, making it ideal for monitoring and analyzing application performance in a microservice architecture. Using these tools requires minimal development effort since they are widely adopted and have strong integrations with AWS.
                                                  Amazon OpenSearch (formerly Elasticsearch) is a fully managed service that makes it easy to store, search, and analyze log data, including traces from distributed systems. It is commonly used to correlate logs and traces because it provides robust querying capabilities and visualization features (via Kibana or OpenSearch Dashboards). This solution offers the necessary tools for log-trace correlation with minimal custom development effort.
                                                  While CloudWatch is suitable for log collection, Amazon Kinesis is more suited for real-time data streaming rather than collecting traces for correlation.
                                                  Kinesis also requires more configuration and development effort compared to OpenTelemetry for tracing.
                                                  Similar to Kinesis, Amazon MSK is primarily used for data streaming rather than trace collection.
                                                  It requires additional setup and custom development for correlation compared to the more straightforward integration provided by FluentBit and OpenTelemetry.
                                                  AWS Glue is primarily used for ETL (extract, transform, load) operations in a data lake. It is not designed for real-time log and trace correlation and would require significant development effort compared to the built-in capabilities of OpenSearch.


                                                  NEW QUESTION # 18
                                                  ......

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