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Snowflake DEA-C01 Prüfungsplan:
| Thema | Einzelheiten |
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| 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.
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| Thema 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.
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| Thema 3 | - 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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| 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.
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| Thema 5 | - 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.
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Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q146-Q151):
146. Frage
A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years. Which solution will meet this requirement?
- A. Configure an S3 Lifecycle policy to move objects that are older than 7 years to S3 Glacier Deep Archive.
- B. Configure an S3 Lifecycle policy to permanently delete objects that are older than 7 years.
- C. Configure an S3 Lifecycle policy to enable S3 Object Lock on all objects that are older than 7 years.
- D. Use Amazon Athena to query the S3 bucket for objects that are older than 7 years. Configure Athena to delete the results.
Antwort: B
Begründung:
An S3 Lifecycle policy can automatically and permanently expire (delete) objects after they reach an age of 7 years, ensuring the data is removed without ongoing manual processes.
147. Frage
Which are the two ways to access elements in a JSON object?
- A. use bracket notation to traverse the path in an object:
<col-umn>['<level1_element>']['<level2_element>']. - B. Use SemiColon notation to traverse a path in a JSON object:
<col-umn>:<level1_element>;<level2_element>;<level3_element>. - C. Use dot notation to traverse a path in a JSON object:
<col-umn>:<level1_element>.<level2_element>.<level3_element>. - D. use Curly bracket notation to traverse the path in an object:
<col-umn>{'<level1_element>'}{'<level2_element>'}.
Antwort: A,C
148. Frage
A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team. Which solution will meet these requirements with the LEAST operational overhead?
- A. Configure Amazon EventBridge rules with distinct event patterns for each error type. Route each error type to a dedicated Amazon Simple Notification Service (Amazon SNS) topic for team- specific alerts.
- B. Use Amazon Simple Queue Service (Amazon SQS) with message attributes to categorize errors.
Allow each team to poll their respective SQS queue for relevant errors. - C. Create an Amazon Simple Notification Service (Amazon SNS) topic with an AWS Lambda function subscriber that evaluates the error type and forwards the error to the appropriate email addresses.
- D. Set up Amazon CloudWatch alarms with different metrics for each error type. Invoke a different Amazon Simple Notification Service (Amazon SNS) notification each time a metrics threshold is crossed.
Antwort: A
Begründung:
Amazon EventBridge is designed to route events based on matching rules and event patterns, so the company can classify each processing error type and immediately send it to the correct destination without custom routing code. By using separate EventBridge rules for security, data validation, and system errors and sending each to its own Amazon SNS topic, the solution stays fully managed, low latency, and operationally simpler than introducing Lambda logic, queue polling, or metric-based alarms.
Reference:
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-event-patterns.html
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-what-is.html
https://docs.aws.amazon.com/sns/latest/dg/welcome.html
149. Frage
A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?
- A. Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.
- B. Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.
- C. Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.
- D. Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.
Antwort: B
150. Frage
A company uses AWS Step Functions to orchestrate a data pipeline. The company has configured the Step Functions logs to push to Amazon CloudWatch Logs when the log level is FATAL The company has enabled logs for all AWS services in the pipeline.
A state named "preprocessing" invokes an AWS Lambda function named "preprocessing." The Lambda function preprocesses data before proceeding to the next state. The company needs to find error details if an error occurs during the data preprocessing.
Which CloudWatch Logs log group should the company check to find the error details?
- A. The Step Functions TaskFailed event in the /aws/vendedlogs/states log group
- B. The Step Functions TaskSucceeded event in the /aws/vendedlogs/states log group
- C. The AWS CloudTrail logs SendTaskFailure event in the CloudTrail/logs/preprocessing log group
- D. The Lambda logs in the laws/lambda/preprocessing log group
Antwort: D
Begründung:
The actual error during data preprocessing occurs inside the AWS Lambda function execution.
Lambda automatically writes detailed execution logs, including errors and stack traces, to its CloudWatch Logs log group. Therefore, checking the Lambda log group provides the precise error details for the preprocessing failure.
151. Frage
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