Free PDF 2026 Snowflake DEA-C01: Updated SnowPro Advanced: Data Engineer Certification Exam Valid Braindumps

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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	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	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q344-Q349):

NEW QUESTION # 344
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

A. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
B. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
C. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
D. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.

Answer: B

Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html

NEW QUESTION # 345
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
B. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
C. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
D. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

Answer: A

NEW QUESTION # 346
A stock broking firm is working on a new application that stores and updates stock market data in Amazon DynamoDB. Different applications will track these changes in real-time. The team is looking for a tool to capture this changing data, which should be available for processing for up to one year. Streaming data should be cleansed, transformed, and stored in Amazon S3 data lakes.
Which service can be considered?

A. Use Kinesis Data Streams for DynamoDB as a streaming model. Process stream records with DynamoDB Streams Kinesis adapter.
B. Use Kinesis Data Streams for DynamoDB as a streaming model. Process stream records with AWS Glue streaming ETL.
C. Use DynamoDB Streams as a streaming model. Process stream records with AWS Data Firehose.
D. Use DynamoDB Streams as a streaming model. Process stream records with AWS Lambda.

Answer: B

Explanation:
Amazon DynamoDB offers two streaming models for change data capture,
Kinesis Data Streams for DynamoDB: Data retention with this model is 1 year. It can process stream records using AWS Lambda, Amazon Managed Service for Apache Flink, Kinesis data firehose, or AWS Glue streaming ETL. AWS Glue Streaming ETL can be used to cleanse, and transform streaming data and store it in either Amazon S3 data lakes or JDBC data stores.
DynamoDB Streams: This model retains data for 24 hours. It can process stream records using AWS Lambda and the DynamoDB Streams Kinesis adapter.
Reference:
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/streamsmain.html

NEW QUESTION # 347
A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises server are the same as the logs that are stored in the S3 bucket. Which solution will meet this requirement?

A. Use the AWS SDK to automatically compute CRC32 checksums during the upload. Store the checksums in S3 object metadata.
B. Create an AWS Lambda function to calculate SHA-256 checksums. Store results in a separate metadata table. Validate the logs after the upload.
C. After uploading the objects to the S3 bucket, enable S3 Object Lock in governance mode on the S3 objects.
D. Enable S3 Object Lock in compliance mode on the S3 bucket. Upload the objects to the bucket.

Answer: A

Explanation:
Computing a checksum during upload and storing it with the object provides an integrity value that can be compared later to confirm the uploaded object matches the original log file. Using an SDK-managed CRC32 checksum gives a straightforward, automated validation approach without additional processing infrastructure.

NEW QUESTION # 348
A SQL UDF evaluates an arbitrary SQL expression and returns the result(s) of the expression. Which value type it can returns?

A. A Set of Rows
B. Single Value
C. Regex
D. Scaler or Tabular depend on input SQL expression

Answer: D

NEW QUESTION # 349
......

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