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2025 Accurate DEA-C01 Flexible Testing Engine | 100% Free SnowPro Advanced: Data Engineer Certification Exam Real Questions

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic
Details

Topic 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.

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 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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q120-Q125):

NEW QUESTION # 120
A company wants to migrate an application and an on-premises Apache Kafka server to AWS.
The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?

A. Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster
B. Amazon Kinesis Data Firehose
C. Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless
D. Amazon Kinesis Data Streams

Answer: C

NEW QUESTION # 121
Snowpipe API provides a REST endpoint for defining the list of files to ingest that Informs Snow-flake about the files to be ingested into a table. A successful response from this endpoint means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. What is name of this Endpoint?

A. REST endpoints--> insertfiles
B. REST endpoints --> ingestfiles
C. REST endpoints --> loadHistoryScan
D. REST endpoints --> insertReport

Answer: A

Explanation:
Explanation
The Snowpipe API provides a REST endpoint for defining the list of files to ingest.
Endpoint: insertFiles
Informs Snowflake about the files to be ingested into a table. A successful response from this end-point means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. For more details, see the response codes below.
In most cases, Snowflake inserts fresh data into the target table within a few minutes.
To Know more about SnowFlake Rest API used for Data File ingestion, do refer:
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-rest-apis.html#data-file-ingestion

NEW QUESTION # 122
A retail company stores data from a product lifecycle management (PLM) application in an on- premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on- premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

A. Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
B. Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
C. Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
D. Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.

Answer: B

Explanation:
https://aws.amazon.com/ko/blogs/apn/change-data-capture-from-on-premises-sql-server-to- amazon-redshift-target/

NEW QUESTION # 123
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

A. Use Amazon S3 as a persistent data store.
B. Use Spot Instances for all primary nodes.
C. Use Hadoop Distributed File System (HDFS) as a persistent data store.
D. Use x86-based instances for core nodes and task nodes.
E. Use Graviton instances for core nodes and task nodes.

Answer: C,E

NEW QUESTION # 124
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.
B. Transform the data that is in the S3 bucket to Apache Parquet format.
C. Use Athena partition projection based on the S3 bucket prefix.
D. Create an AWS Glue partition index. Enable partition filtering.
E. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.

Answer: C,D

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren't using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions' metadata to the AWS Glue table.

NEW QUESTION # 125
......

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