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Snowflake DEA-C01 Exam Syllabus Topics:

SectionObjectives
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
          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
                  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
                          Performance Optimization- Warehouse optimization
                          • 1. Scaling strategies
                            • 2. Concurrency management
                              - Query performance tuning
                              • 1. Caching mechanisms
                                • 2. Clustering and micro-partitions
                                  Semi-Structured and Advanced Data Handling- Semi-structured data processing
                                  • 1. JSON, Avro, Parquet handling
                                    • 2. VARIANT data type usage
                                      - Advanced transformation techniques
                                      • 1. Flattening nested data
                                        • 2. Complex transformations in SQL
                                          Data Engineering Fundamentals on Snowflake- Data ingestion concepts
                                          • 1. Batch loading strategies
                                            • 2. Continuous ingestion concepts
                                              - Snowflake architecture for data engineering
                                              • 1. Virtual warehouses and compute scaling
                                                • 2. Storage and compute separation

                                                  >> DEA-C01 Valid Practice Questions <<

                                                  2026 Snowflake Trustable DEA-C01: SnowPro Advanced: Data Engineer Certification Exam Valid Practice Questions

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                                                  Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q368-Q373):

                                                  NEW QUESTION # 368
                                                  A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
                                                  A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
                                                  Which solution will meet this requirement with the LEAST manual intervention?

                                                  Answer: C

                                                  Explanation:
                                                  Amazon Macie is a fully managed data security service that automatically discovers, classifies, and protects sensitive data such as personally identifiable information (PII) in Amazon S3.
                                                  Macie's automated sensitive data discovery feature can scan S3 objects for PII without requiring manual intervention, making it the most efficient and automated solution for identifying sensitive data before sharing it with business partners.
                                                  CloudTrail is useful for monitoring API calls but is not designed to inspect the contents of S3 objects for PII. This would require manual effort to inspect the data saved in S3, which is inefficient.
                                                  While Lambda could be used to scan for PII, it would require custom code to identify sensitive data. This approach increases manual effort for development and maintenance compared to using a fully managed service like Macie.
                                                  Writing custom SQL queries to identify PII is a manual process and requires the creation and management of Glue tables and Athena queries. This approach adds complexity and manual intervention compared to the automated capabilities of Macie.


                                                  NEW QUESTION # 369
                                                  A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
                                                  The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
                                                  Which extract, transform, and load (ETL) service will meet these requirements?

                                                  Answer: C

                                                  Explanation:
                                                  Glue is like the more good-looking one, but weaker brother of EMR. So when it's about petabyte scales, let EMR do the work and have Glue stay away from the action.


                                                  NEW QUESTION # 370
                                                  A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.
                                                  Which recommendations apply to this scenario? (Select THREE)

                                                  Answer: A,C,E

                                                  Explanation:
                                                  Explanation
                                                  The recommendations that apply to this scenario are:
                                                  Ensure that data files are 100-250 MB (or larger) in size compressed: This recommendation will improve Snowpipe performance by reducing the number of files that need to be loaded and increasing the parallelism of loading. Smallerfiles can cause performance degradation or errors due to excessive metadata operations or network latency.
                                                  Verify each value of each unique element stores a single native data type (string or number): This recommendation will improve Snowpipe performance by avoiding data type conversions or errors when loading JSON data into variant columns. Snowflake supports two native data types for JSON elements:
                                                  string and number. If an element has mixed data types across different files or records, such as string and boolean, Snowflake will either convert them to string or raise an error, depending on the FILE_FORMAT option.
                                                  Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute: This recommendation will minimize Snowpipe costs by reducing the number of notifications that need to be sent to Snowpipe for auto-ingestion. Snowpipe charges for notifications based on the number of files per notification and the frequency of notifications. By creating smaller files and staging them at a lower frequency, fewer notifications will be needed.


                                                  NEW QUESTION # 371
                                                  A company uses AWS Glue Apache Spark jobs to handle extract, transform, and load (ETL) workloads. The company has enabled logging and monitoring for all AWS Glue jobs.
                                                  One of the AWS Glue jobs begins to fail. A data engineer investigates the error and wants to examine metrics for all individual stages within the job.
                                                  How can the data engineer access the stage metrics?

                                                  Answer: A

                                                  Explanation:
                                                  AWS Glue provides a built-in Spark UI (accessible from the Glue console's job run details) that exposes per-stage metrics, such as task counts, shuffle read/write sizes, and execution times, for each Spark stage. This is the most direct way to drill down into the individual stages when troubleshooting a failing Glue Spark job.


                                                  NEW QUESTION # 372
                                                  A company needs a solution to automatically ingest data from an Amazon S3 bucket to an Amazon Redshift table every day. The company must obfuscate sensitive data in Amazon Redshift. Users must not be able to view the sensitive data as plaintext.
                                                  Which solution will meet these requirements with the LEAST operational overhead?

                                                  Answer: D

                                                  Explanation:
                                                  Schedule an AWS Glue job to load S3 data into Amazon Redshift, then use Redshift's built-in sensitive data discovery and dynamic data masking to identify sensitive columns and enforce masking at query time. This meets the daily ingest and non-plaintext visibility requirements with the least operational overhead, since masking is managed natively in Redshift without custom obfuscation pipelines or Macie-based preprocessing.


                                                  NEW QUESTION # 373
                                                  ......

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