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Snowflake DEA-C01 Prüfungsplan:
| Thema | Einzelheiten |
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| Thema 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.
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| Thema 2 | - 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 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.
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| Thema 4 | - 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 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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DEA-C01: SnowPro Advanced: Data Engineer Certification Exam Dumps & PassGuide DEA-C01 Examen
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Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q49-Q54):
49. Frage
A media streaming company collects massive amounts of log data from their applications running on AWS. They want to analyze these logs in real-time to monitor performance, detect anomalies, and troubleshoot issues. The company decides to use Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) to index and search through their log data. They also need a solution to visualize this data and create dashboards for their operations team to monitor key metrics and trends.
Which approach should the media streaming company use to analyze and visualize their log data effectively using Amazon OpenSearch Service?
- A. Use AWS Lambda to process log data and store it in Amazon RDS, then create dashboards with Amazon QuickSight.
- B. Use Amazon Data Firehose to stream log data to Amazon OpenSearch Service and use OpenSearch Dashboards to create visualizations.
- C. Use Amazon CloudWatch Logs to store log data and generate metrics for visualization in Amazon CloudWatch Dashboards.
- D. Use AWS Glue to transform log data and store it in Amazon S3, then query the data with Amazon Athena.
Antwort: B
Begründung:
Amazon Data Firehose can stream log data in near real-time to Amazon OpenSearch Service, where it can be indexed and searched. OpenSearch Dashboards provide powerful tools for visualizing this data, making it the most effective solution for real-time log analysis and visualization.
References:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/what-is.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/kibana.html
https://aws.amazon.com/blogs/big-data/streaming-data-into-your-amazon-elasticsearch-service-domain-with-amazon-kinesis-firehose/
50. Frage
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications?
(Choose two.)
- A. SQL
- B. Gremlin
- C. Spark SQL
- D. ANSI SQL
- E. SPARQL
Antwort: B,E
51. Frage
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.
- B. Implement custom automation scripts in AWS Secrets Manager.
- C. Store self-managed certificates on the EC2 instances.
- D. Use AWS Certificate Manager (ACM).
Antwort: D
Begründung:
AWS Certificate Manager (ACM) simplifies the process of generating, distributing, and managing SSL/TLS certificates. ACM allows you to automatically renew and deploy certificates with minimal operational overhead. Once integrated with services like EC2, ACM can handle the entire lifecycle of the certificates, from creation to renewal and deployment, without manual intervention.
Managing certificates manually introduces significant operational overhead. You would need to handle generation, deployment, renewal, and rotation manually, which is error-prone and time- consuming.
While you could store certificates in AWS Secrets Manager, you would still need to implement custom scripts for certificate generation, rotation, and deployment, increasing operational complexity compared to ACM.
Amazon ECS Service Connect is used for simplifying service discovery and communication within ECS. While it can secure traffic between ECS services, it is not designed for managing SSL/TLS certificates on EC2 instances.
52. Frage
A company uses Amazon Redshift as its data warehouse service. A data engineer needs to design a physical data model.
The data engineer encounters a de-normalized table that is growing in size. The table does not have a suitable column to use as the distribution key.
Which distribution style should the data engineer use to meet these requirements with the LEAST maintenance overhead?
- A. ALL distribution
- B. AUTO distribution
- C. KEY distribution
- D. EVEN distribution
Antwort: B
Begründung:
AUTO distribution allows Amazon Redshift to automatically manage the distribution style based on the size of the table. With this option, Redshift chooses the most optimal distribution method, and as the table grows, it will adjust the distribution style to maintain performance. This minimizes maintenance overhead since the system adapts without the need for manual tuning.
ALL distribution copies the entire table to all nodes. It can be useful for small, static tables but is inefficient for large or growing tables as it increases storage and I/O costs significantly.
EVEN distribution distributes rows evenly across all nodes. While it reduces data skew, it may not be optimal for larger tables where certain queries would benefit from co-locating data to minimize cross-node traffic.
KEY distribution places data based on the values in a specific column (distribution key). Since the question states that no suitable column exists for a distribution key, this method would not be appropriate.
53. Frage
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?
- A. Create a size XL virtual warehouse to support all the dashboard queries Monitor query runtimes to determine whether the virtual warehouse should be resized.
- B. Create a virtual warehouse for every 250 Analysts Monitor to determine how many of these virtual warehouses are being utilized at capacity.
- C. Assign queries to a multi-cluster virtual warehouse with economy auto-scaling Allow the system to automatically start and stop clusters according to demand.
- D. Assign all queries to a multi-cluster virtual warehouse set to maximized mode Monitor to determine the smallest suitable number of clusters.
Antwort: D
Begründung:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.
54. Frage
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