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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 2
  • Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 3
  • Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 4
  • Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 5
  • Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q339-Q344):

NEW QUESTION # 339
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Choose two.)

Answer: B,D

Explanation:
To run AWS Glue Apache Spark jobs with the Apache Iceberg framework, you must:
Set --datalake-formats to iceberg so Glue knows to use the Iceberg format.
Specify the Iceberg library by using the --extra-jars parameter to include the appropriate Iceberg JAR files for Glue to access the Iceberg table format.


NEW QUESTION # 340
A media streaming company collects massive amounts of log data from their applications running on AWS. They want to analyze these logs in real-time to monitor performance, detect anomalies, and troubleshoot issues. The company decides to use Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) to index and search through their log data. They also need a solution to visualize this data and create dashboards for their operations team to monitor key metrics and trends.
Which approach should the media streaming company use to analyze and visualize their log data effectively using Amazon OpenSearch Service?

Answer: D

Explanation:
Amazon Data Firehose can stream log data in near real-time to Amazon OpenSearch Service, where it can be indexed and searched. OpenSearch Dashboards provide powerful tools for visualizing this data, making it the most effective solution for real-time log analysis and visualization.
References:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/what-is.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/kibana.html
https://aws.amazon.com/blogs/big-data/streaming-data-into-your-amazon-elasticsearch-service-domain-with-amazon-kinesis-firehose/


NEW QUESTION # 341
A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets. Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?

Answer: D

Explanation:
Amazon Redshift data sharing allows multiple Redshift clusters (including across AWS accounts) to access centralized datasets in real time without copying or moving data. This provides immediate access while maintaining performance because queries run directly on the producer cluster's managed storage, eliminating duplication and ETL overhead.


NEW QUESTION # 342
A company needs to store semi-structured transactional data for an application in a database.
The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds. Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Amazon DynamoDB is a fully managed serverless database that is designed for millisecond response times and works well for semi-structured application data. Because the application reads frequently and writes infrequently, adding DynamoDB Accelerator (DAX) provides an in- memory cache that further reduces read latency with minimal operational overhead.


NEW QUESTION # 343
A data engineer notices that Amazon Athena queries are held in a queue before the queries run.
How can the data engineer prevent the queries from queueing?

Answer: D

Explanation:
https://aws.amazon.com/blogs/aws/introducing-athena-provisioned-capacity/


NEW QUESTION # 344
......

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