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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q378-Q383):

NEW QUESTION # 378
A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics.
Which solution will give the company the ability to use Spark to access Athena?

Answer: C

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/notebooks-spark.html


NEW QUESTION # 379
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: A

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 # 380
A company built a data lake and a data warehouse on AWS. The company wants to implement a data catalog to enhance the current data storage solutions. The company wants to have the capability to add business metadata and glossary information to the data catalog for every asset.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Amazon DataZone provides a fully managed data catalog and governance service with built-in support for business glossaries, custom metadata forms, and a user-friendly data portal. You can onboard your existing data assets, define glossaries, and capture business metadata without building or maintaining custom tables, APIs, or applications, minimizing operational overhead.


NEW QUESTION # 381
A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports.
The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing.
Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)

Answer: A,C


NEW QUESTION # 382
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse.
The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

Answer: C

Explanation:
The SUPER data type in Amazon Redshift allows you to natively store semi-structured data such as JSON. By using the SUPER data type, you can store JSON data in a Redshift table without needing to flatten or transform it beforehand. This approach requires the least effort because it allows you to directly load the JSON data and query it using Redshift's JSONPath and PARTITION BY capabilities without additional ETL processing.


NEW QUESTION # 383
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