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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Related Certifications:SnowPro Core Certification (Recommended Prerequisite)
Exam Price:$375 USD
Available Languages:English
Passing Score:~70% (exact passing score not publicly disclosed by Snowflake)
Certificate Validity Period:2 years (recertification required)
Exam Format:Multiple Choice, Multiple Select
Exam Duration:90 minutes
Real Exam Qty:approximately 50 questions
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:https://www.snowflake.com/certification/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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 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 (Q336-Q341):

NEW QUESTION # 336
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.

Answer: A,C,E

Explanation:
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.


NEW QUESTION # 337
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?

Answer: A

Explanation:
The error "No space left on device" during a sort-merge join typically results from insufficient disk space during the shuffle phase of a Spark job. Using the AWS Glue Spark shuffle manager (optimized shuffle manager) helps reduce disk usage and improves shuffle performance by optimizing how intermediate data is written and read. This directly addresses the issue without requiring job logic changes or manual resource management.


NEW QUESTION # 338
A global ecommerce company occasionally receives customer data files in its Amazon S3 data lake. The company needs to automatically detect new data and mask sensitive data before making the data available to the company's analytics team.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
Amazon EventBridge can detect new object events in Amazon S3 and trigger an automated workflow. AWS Glue workflows provide a managed way to orchestrate the processing, and AWS Glue DataBrew can detect and mask sensitive data before writing the cleansed output back to Amazon S3. This meets the requirement with the least operational overhead because it uses managed services for both event detection and data masking, whereas Amazon Macie is primarily for discovering and reporting sensitive data in S3 rather than masking it in-place as part of the preparation pipeline.


NEW QUESTION # 339
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

Answer: C

Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/register-data-lake.html
https://docs.aws.amazon.com/lake-formation/latest/dg/registration-role.html


NEW QUESTION # 340
Elon, a Data Engineer, needs to Split Semi-structured Elements from the Source files and load them as an array into Separate Columns.
Source File:
1.+----------------------------------------------------------------------+
2.| $1 |
3.|----------------------------------------------------------------------|
4.| {"mac_address": {"host1": "197.128.1.1","host2": "197.168.0.1"}}, |
5.| {"mac_address": {"host1": "197.168.2.1","host2": "197.168.3.1"}} |
6.+----------------------------------------------------------------------+ Output: Splitting the Machine Address as below.
1.COL1 | COL2 |
2.|----------+----------|
3.| [ | [ |
4.| "197", | "197", |
5.| "128", | "168", |
6.| "1", | "0", |
7.| "1" | "1" |
8.| ] | ] |
9.| [ | [ |
10.| "197", | "197", |
11.| "168", | "168", |
12.| "2", | "3", |
13.| "1" | "1" |
14.| ] | ]
Which SnowFlake Function can Elon use to transform this semi structured data in the output for-mat?

Answer: C


NEW QUESTION # 341
......

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