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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
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 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 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q294-Q299):

NEW QUESTION # 294
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions?

Answer: B

Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions. The reason is that the owner of the data being shared does not typically control the users or roles in the account with which the view is being shared.


NEW QUESTION # 295
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A


NEW QUESTION # 296
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead?
(Choose two.)

Answer: A,E

Explanation:
AWS Glue triggers provide a simple and integrated way to schedule ETL jobs. By configuring these triggers to run hourly, the data engineer can ensure that the data processing and updates occur as required without the need for external scheduling tools or custom scripts. This approach is directly integrated with AWS Glue, reducing the complexity and operational overhead.
AWS Glue supports connections to various data sources, including Amazon RDS and MongoDB.
By using AWS Glue connections, the data engineer can easily configure and manage the connectivity between these data sources and Amazon Redshift. This method leverages AWS Glue's built-in capabilities for data source integration, thus minimizing operational complexity and ensuring a seamless data flow from the sources to the destination (Amazon Redshift).


NEW QUESTION # 297
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution.
The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?

Answer: C

Explanation:
Amazon Athena federated query allows querying data from multiple data sources, including Amazon Redshift, Teradata, and Google BigQuery, using their federated query connectors. This solution offers a serverless approach, reducing the operational overhead of managing infrastructure while allowing SQL-based transformations across all data sources. Once the data is read and joined, Athena can write the results back to Amazon S3 in the Iceberg table format with a Merge operation.
This approach minimizes the operational effort as Athena manages the complexity of connecting to different databases through its connectors, and you can perform the necessary transformations and data joins using familiar SQL.
While AWS Glue is a powerful ETL tool, it requires more operational effort to manage complex transformations across multiple systems, and managing native transforms across different engines (Redshift, Teradata, BigQuery) in Glue can introduce additional complexity.
Amazon EMR with PySpark can handle the task, but it requires more operational effort to manage and maintain the EMR cluster. Writing and maintaining PySpark code can also be more complex compared to using SQL in Athena.
Appflow is primarily designed for simple data movement between SaaS applications and AWS services, but it does not provide the complex transformation and joining capabilities needed for this scenario. Using Athena after Appflow for joins adds unnecessary complexity compared to directly using federated queries in Athena.


NEW QUESTION # 298
A data engineer must maintain and monitor a data pipeline on AWS that processes streaming data from Internet of Things (IoT) devices. The pipeline uses Amazon Kinesis Data Streams to ingest data and Amazon Data Firehose to deliver data to an Amazon S3 bucket. The data engineer needs to monitor the health of the pipeline. Which solution will meet these requirements with the LEAST operational effort?

Answer: A

Explanation:
CloudWatch metrics for Kinesis Data Streams and Firehose are available automatically and provide direct visibility into ingestion, throughput, and delivery success. Configuring CloudWatch alarms on key metrics (such as stream input/output and Firehose delivery success) enables proactive, automated health monitoring and alerting with minimal setup and no custom code or additional services to operate.


NEW QUESTION # 299
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