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| Topic | Details |
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| 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.
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| Topic 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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| Topic 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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| Topic 4 | - 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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| Topic 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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Quiz 2026 Snowflake Latest DEA-C01: Exam SnowPro Advanced: Data Engineer Certification Exam Study Solutions
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q175-Q180):
NEW QUESTION # 175
A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.
The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total.
Which solution will meet these requirements with the LEAST operational cost?
- A. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.
- B. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
- C. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
- D. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
Answer: D
Explanation:
Standard Step Functions support long-running, sequential workflows and the Wait for Callback pattern without charging for idle wait time, minimizing cost while ensuring completion within 24 hours. Express would bill for long waits, Lambda+EventBridge adds complexity and limits, and MWAA has higher operational overhead and cost.
NEW QUESTION # 176
A company creates a new non-production application that runs on an Amazon EC2 instance. The application needs to communicate with an Amazon RDS database instance using Java Database Connectivity (JDBC). The EC2 instances and the RDS database instance are in the same subnet.
Which solution will meet this requirement?
- A. Modify the ec2_authorized_hosts parameter in the RDS parameter group to include the EC2 instances. Restart the database instance.
- B. Update the database security group to allow connections from the EC2 instances.
- C. Modify the IAM role that is assigned to the database instance to allow connections from the EC2 instances.
- D. Enable the Amazon RDS Data API and specify the Amazon Resource Name (ARN) of the database instance in the JDBC connection string.
Answer: B
Explanation:
Connectivity from Amazon EC2 to an Amazon RDS DB instance in the same subnet or VPC is controlled by VPC security groups. Updating the database security group to allow inbound connections from the EC2 instances enables the application to connect to the database by using JDBC. IAM roles do not control direct JDBC network access, and the RDS Data API is not used through a JDBC connection string.
Reference:
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Overview.RDSSecurityGroups.html
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_VPC.Scenarios.html
NEW QUESTION # 177
An investment company needs to manage and extract insights from a volume of semi-structured data that grows continuously.
A data engineer needs to deduplicate the semi-structured data, remove records that are duplicates, and remove common misspellings of duplicates.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use non-Windows functions in Amazon Athena to remove duplicate records.
- B. Use the global tables feature of Amazon DynamoDB to prevent duplicate data.
- C. Use Amazon Neptune ML and an Apache Gremlin script to remove duplicate records.
- D. Use the FindMatches feature of AWS Glue to remove duplicate records.
Answer: D
NEW QUESTION # 178
A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.
After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?
- A. Enable clustering on the table
- B. Increase the warehouse size
- C. Rewrite the query to remove the CTEs.
- D. Switch to a multi-cluster virtual warehouse
Answer: B
Explanation:
Explanation
The recommended approach to maximize performance of this query if the Profile shows data spillage is to increase the warehouse size. Data spillage occurs when the query requires more memory than the warehouse can provide and has to spill some intermediate results to disk. This can degrade the query performance by increasing the disk IO time. Increasing the warehouse size can increase the amount of memory available for the query and reduce or eliminate data spillage.
NEW QUESTION # 179
Data Engineer Loading File named snowdata.tsv in the /datadir directory from his local machine to Snowflake stage and try to prefix the file with a folder named tablestage, please mark the correct command which helps him to load the files data into snowflake internal Table stage?
- A. put file://c:\datadir\snowdata.tsv @%tablestage;
- B. put file:///datadir/snowdata.tsv @%tablestage;
- C. put file://c:\datadir\snowdata.tsv @~/tablestage;
- D. put file://c:\datadir\snowdata.tsv @tablestage;
Answer: A
Explanation:
Explanation
Execute PUT to upload (stage) local data files into an internal stage.
@% character combination identifies a table stage.
NEW QUESTION # 180
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