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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	Approximately 65 questions
Exam Price:	$375 USD
Certificate Validity Period:	2 years
Exam Format:	Scenario-based questions, Multiple choice, Multiple select
Exam Duration:	115 minutes
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Related Certifications:	SnowPro Core Certification
Available Languages:	English
Recommended Training:	Snowflake University Training SnowPro Advanced Data Engineer Exam Guide
Exam Registration:	Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or authorized testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data. Which solution will meet this requirement in the MOST operationally efficient way?

A. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.
B. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
C. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.
D. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.

Answer: D

Explanation:
AWS Glue DataBrew provides built-in data profiling specifically for exploring dataset structure, data types, distributions, missing values, and anomalies before preparation. For a 100 MB Parquet file in Amazon S3, creating a profile job is the most operationally efficient approach because it requires minimal setup and no custom infrastructure or query framework.

NEW QUESTION # 105
A retail company stores transactions, store locations, and customer information tables in four reserved ra3.4xlarge Amazon Redshift cluster nodes. All three tables use even table distribution.
The company updates the store location table only once or twice every few years.
A data engineer notices that Redshift queues are slowing down because the whole store location table is constantly being broadcast to all four compute nodes for most queries. The data engineer wants to speed up the query performance by minimizing the broadcasting of the store location table.
Which solution will meet these requirements in the MOST cost-effective way?

A. Upgrade the Redshift reserved node to a larger instance size in the same instance family.
B. Change the distribution style of the store location table to KEY distribution based on the column that has the highest dimension.
C. Change the distribution style of the store location table from EVEN distribution to ALL distribution.
D. Add a join column named store_id into the sort key for all the tables.

Answer: C

Explanation:
Changing the distribution style of the store location table to ALL distribution (A) is the most cost- effective solution. It directly addresses the issue of broadcasting by ensuring the entire table is available on each node, significantly improving join performance without incurring substantial additional costs.

NEW QUESTION # 106
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?

A. Use Python and Pandas in an AWS Glue Studio notebook. Ensure that the data engineers add additional transformations to complete the pipeline.
B. Use AWS Glue Studio with data preparation recipe transformations. Ensure that the data engineers add additional transformations to complete the pipeline.
C. Create a document that includes the data preparation rules. Ensure that the data engineers implement the rules in AWS Glue.
D. Use Amazon SageMaker Canvas and SageMaker Data Wrangler to write to a new dataset.
Ensure that the data engineers add additional transformations to complete the pipeline by using AWS Glue.

Answer: B

Explanation:
AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.

NEW QUESTION # 107
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. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
B. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
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: B

NEW QUESTION # 108
A Data Engineer is working on a Snowflake deployment in AWS eu-west-1 (Ireland). The Engineer is planning to load data from staged files into target tables using the copy into command Which sources are valid? (Select THREE)

A. External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt)
B. Internal stage on GCP us-central1 (Iowa)
C. External stage on GCP us-central1 (Iowa)
D. External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland)
E. Internal stage on AWS eu-central-1 (Frankfurt)
F. SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland)

Answer: A,C,D

Explanation:
Explanation
The valid sources for loading data from staged files into target tables using the copy into command are:
External stage on GCP us-central1 (Iowa): This is a valid source because Snowflake supports cross-cloud data loading from external stages on different cloud platforms and regions than the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-west-1 (Ireland): This is a valid source because Snowflake supports data loading from external stages on the same cloud platform and region as the Snowflake deployment.
External stage in an Amazon S3 bucket on AWS eu-central 1 (Frankfurt): This is a valid source because Snowflake supports cross-region data loading from external stages on different regions than the Snowflake deployment within the same cloud platform. The invalid sources are:
Internal stage on GCP us-central1 (Iowa): This is an invalid source because internal stages are always located on the same cloud platform and region as the Snowflake deployment. Therefore, an internal stage on GCP us-central1 (Iowa) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
Internal stage on AWS eu-central-1 (Frankfurt): This is an invalid source because internal stages are always located on the same region as the Snowflake deployment. Therefore, an internal stage on AWS eu-central-1 (Frankfurt) cannot be used for a Snowflake deployment on AWS eu-west-1 (Ireland).
SSO attached to an Amazon EC2 instance on AWS eu-west-1 (Ireland): This is an invalid source because SSO stands for Single Sign-On, which is a security integration feature in Snowflake, not a data staging option.

NEW QUESTION # 109
......

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