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Snowflake DEA-C01 Exam Syllabus Topics:

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

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                                                  Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q85-Q90):

                                                  NEW QUESTION # 85
                                                  A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
                                                  The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
                                                  Which solution will meet these requirements with the LEAST operational overhead?

                                                  Answer: A


                                                  NEW QUESTION # 86
                                                  A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
                                                  An AWS Glue crawler updates the partitions.
                                                  The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
                                                  Which solution will meet these requirements?

                                                  Answer: B

                                                  Explanation:
                                                  By including predicates on your partition columns in each Athena query (for example, WHERE year = '2025' AND month = '05'), Athena prunes partitions at the optimizer level and reads only the matching S3 folders. This directly minimizes the data scanned and improves query efficiency with no additional infrastructure changes.


                                                  NEW QUESTION # 87
                                                  A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
                                                  Which solution will meet this requirement MOST cost-effectively?

                                                  Answer: C

                                                  Explanation:
                                                  You can query these sources by using Federated Queries, which is a native feature of Athena.
                                                  The other options may increase costs and operational overhead, as they use more than one service to achieve the same result.
                                                  https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html


                                                  NEW QUESTION # 88
                                                  A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of each data file.
                                                  The company loads all the data files into one table in the Redshift cluster by using a separate COPY command for each data file location. This approach takes a long time to load all the data files into the table. The company must increase the speed of the data ingestion. The company does not want to increase the cost of the process.
                                                  Which solution will meet these requirements?

                                                  Answer: B

                                                  Explanation:
                                                  https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-single-copy-command.html
                                                  https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html


                                                  NEW QUESTION # 89
                                                  An online retailer uses multiple delivery partners to deliver products to customers. The delivery partners send order summaries to the retailer. The retailer stores the order summaries in Amazon S3.
                                                  Some of the order summaries contain personally identifiable information (PII) about customers. A data engineer needs to detect PII in the order summaries so the company can redact the PII.
                                                  Which solution will meet these requirements with the LEAST operational overhead?

                                                  Answer: B

                                                  Explanation:
                                                  Amazon Macie is a fully managed data security and privacy service that uses machine learning and pattern matching to discover and protect sensitive data, such as Personally Identifiable Information (PII), stored in Amazon S3. Macie can automatically scan the order summaries for PII, enabling the company to detect and then redact PII as required.


                                                  NEW QUESTION # 90
                                                  ......

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