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Snowflake DEA-C01 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • 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.
Thema 2
  • 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.
Thema 3
  • 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.
Thema 4
  • 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.
Thema 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q37-Q42):

37. Frage
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?

Antwort: E

Begründung:
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.


38. Frage
A company stores information about its subscribers in an Amazon S3 bucket. The company runs an analysis every time a subscriber ends their subscription. The company uses AWS Lambda functions to respond to events from the S3 bucket by performing analyses.
The Lambda functions clean data from the S3 bucket and initiate an AWS Glue workflow. The Lambda functions have 128 MB of memory and 512 MB of ephemeral storage. The Lambda functions have a timeout of 15 seconds.
All three functions successfully finish running. However, CPU usage is often near 100%, which causes slow performance. The company wants to improve the performance of the functions and reduce the total runtime of the pipeline.
Which solution will meet these requirements?

Antwort: A

Begründung:
In AWS Lambda, increasing memory also proportionally increases the allocated CPU. By raising the Lambda functions' memory from 128 MB to 512 MB, the functions receive more CPU power, which reduces execution time and improves performance without changing logic or timeouts.


39. Frage
Select the Correct statements with regard to using Federated authentication/SSO?

Antwort: A,B,C,D


40. Frage
When processing large datasets using distributed computing frameworks, uneven distribution of data can lead to processing delays. What is this phenomenon commonly known as?

Antwort: C


41. Frage
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Choose two.)

Antwort: C,D

Begründung:
One common issue could be that the table's S3 location is incorrect or has changed. The data engineer should check that the Athena table is correctly referencing the S3 bucket or path where the CloudTrail logs are stored.
If new partitions have been added to the S3 bucket (such as logs for a new year), but these partitions haven't been reflected in the Athena table, running the MSCK REPAIR TABLE command will load new partitions into the table's metadata, ensuring that Athena can query the new data.
A query timeout would not cause data to be missing from the results. This option does not address the issue of missing data in the query results.
Athena is a serverless query service, so there is no option to "restart" Athena. This is not applicable.
While deleting and recreating the table could resolve the issue, it's generally not necessary and is more disruptive. The problem can often be resolved with less intrusive steps like checking the S3 location and repairing partitions.


42. Frage
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