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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 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 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.
Topic 4
  • 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 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
When processing large datasets using distributed computing frameworks, uneven distribution of data can lead to processing delays. What is this phenomenon commonly known as?

Answer: C


NEW QUESTION # 83
For SQL UDFs, The invoker of the function need not have access to the objects referenced in the function definition, but only needs the privilege to use the function?

Answer: B


NEW QUESTION # 84
A data engineer configures a large number of AWS Glue jobs that all start up around the same time. All the jobs run for less than 1 hour in the same subnet of the same VPC. All the AWS Glue jobs run on a G1.X worker type.
Some of the jobs occasionally fail with the following error: "The specified subnet does not have enough free addresses to satisfy the request".
What is the likely root cause of the error?

Answer: B

Explanation:
Each AWS Glue worker requires an elastic network interface (ENI) and consumes an IP address in the subnet. When a large number of Glue jobs start simultaneously, the subnet can run out of available IP addresses. This causes the error "The specified subnet does not have enough free addresses." The root cause is insufficient IPs in the subnet, not the VPC overall.


NEW QUESTION # 85
A Data Engineer is implementing a near real-time ingestionpipeline to toad data into Snowflake using the Snowflake Kafka connector. There will be three Kafka topics created.
......snowflake objects are created automatically when the Kafka connector starts? (Select THREE)

Answer: B,E,F

Explanation:
Explanation
The Snowflake objects that are created automatically when the Kafka connector starts are tables, pipes, and internal stages. The Kafka connector will create one table, one pipe, and oneinternal stage for each Kafka topic that is configured in the connector properties. The table will store the data from the Kafka topic, the pipe will load the data from the stage to the table using COPY statements, and the internal stage will store the files that are produced by the Kafka connector using PUT commands. The other options are not Snowflake objects that are created automatically when the Kafka connector starts. Option B, tasks, are objects that can execute SQL statements on a schedule without requiring a warehouse. Option E, external stages, are objects that can reference locations outside of Snowflake, such as cloud storage services. Option F, materialized views, are objects that can store the precomputed results of a query and refresh them periodically.


NEW QUESTION # 86
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

Answer: D

Explanation:
The RootDiskUsed metric in Amazon MSK indicates that the storage space on the broker's disk is filling up. To resolve this, the company can expand the storage for the MSK brokers to prevent them from running out of disk space.
Amazon MSK supports automatic storage scaling, which can be configured to avoid similar issues in the future. By enabling auto-scaling, MSK will automatically expand the storage when needed, ensuring that the brokers have enough space to handle new topics or additional data.
ZooKeeper is used for managing the state of Kafka brokers, but the RootDiskUsed metric pertains to the Kafka brokers, not the ZooKeeper nodes.
Expanding ZooKeeper storage will not resolve the issue.
While increasing the instance size may improve performance, it does not address the issue of running out of disk space on the broker's root volume, which is causing the CloudWatch alarm.
Kafka topics do not have a direct volume specification. Storage for Kafka is managed at the broker level, not at the individual topic level. Therefore, this option does not apply to the issue of disk usage.


NEW QUESTION # 87
......

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