Quiz 2026 Snowflake DEA-C01: SnowPro Advanced: Data Engineer Certification Exam First-grade 100% Exam Coverage

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 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.
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.
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.
Topic 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.
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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q256-Q261):

NEW QUESTION # 256
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte- scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Add an Amazon ElastiCache cluster between the BI application and Athena.
B. Use the query result reuse feature of Amazon Athena for the SQL queries.
C. Change the format of the files that are in the dataset to Apache Parquet.
D. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day.

Answer: B

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html
Use the Query Result Reuse Feature of Amazon Athena. This leverages Athena's built-in feature to reduce redundant data scans and thus lowers query costs.

NEW QUESTION # 257
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. Enable the Amazon RDS Data API and specify the Amazon Resource Name (ARN) of the database instance in the JDBC connection string.
B. Modify the IAM role that is assigned to the database instance to allow connections from the EC2 instances.
C. Update the database security group to allow connections from the EC2 instances.
D. Modify the ec2_authorized_hosts parameter in the RDS parameter group to include the EC2 instances. Restart the database instance.

Answer: C

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 # 258
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. 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.
B. Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.
C. Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.
D. 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.

Answer: A

NEW QUESTION # 259
A company maintains a central Amazon Redshift data warehouse that aggregates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer notices that some complex transformation queries take hours to finish. The data engineer wants to optimize query performance to reduce query execution time as much as possible.
Which solution will meet this requirement?

A. Export the tables to an Amazon S3 bucket. Use Amazon Athena to query the data in the bucket.
B. Increase the concurrency scaling quota for the Redshift cluster.
C. Use materialized views in Amazon Redshift for frequently queried data patterns.
D. Use Amazon Redshift Spectrum to create external tables based on the Redshift tables.

Answer: C

Explanation:
Amazon Redshift materialized views precompute and store results of complex queries, allowing subsequent queries to run much faster by reading from the precomputed data. This significantly reduces execution time for frequently used transformation patterns.

NEW QUESTION # 260
Jeff, a Data Engineer, accessing elements in JSON object in its 3 data loading scripts, he unknow-ingly use the upper case while accessing the elements. e.g.
Script 1 --> fruits:apple.sweet
Script 2 --> FRUITS:apple.sweet
Script 3 --> FRUITS:Apple.Sweet
Which are the correct statements?

A. Script 1 & 3 traverse path will be treated in same way.
B. Script 1,2,3 traverse path will treat in same way.
C. Script 1 & Script 2 traverse path twill be treated same, but Script 2 will not.
D. Script 2&3 traverse path will be same.

Answer: C

Explanation:
Explanation
There are two ways to access elements in a JSON object:
Dot Notation
Bracket Notation
Regardless of which notation you use, the column name is case-insensitive but element names are case-sensitive.

NEW QUESTION # 261
......

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