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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Exam Duration:115 minutes
Passing Score:Not publicly disclosed (Snowflake uses scaled scoring)
Real Exam Qty:Approximately 65 questions
Available Languages:English
Certificate Validity Period:2 years
Exam Format:Multiple choice, Scenario-based questions, Multiple select
Related Certifications:SnowPro Core Certification
Exam Price:$375 USD
Recommended Training:SnowPro Advanced Data Engineer Exam Guide
Snowflake University Training
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:

TopicDetails
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
  • 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
  • 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 (Q37-Q42):

NEW QUESTION # 37
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

Answer: C

Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. Using DataBrew recipes, you can easily perform transformations such as renaming columns, removing specific columns, ignoring certain rows, creating new columns, and filtering data based on column values. This solution requires the least development effort because it provides a no-code/low-code interface for performing these tasks.
While AWS Glue Python jobs can handle these transformations, they would require writing custom code, which involves more development effort compared to using DataBrew.
AWS Glue crawlers are used for cataloging data and are not suitable for performing complex transformations like ignoring rows, renaming columns, or creating new columns.
Using an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files adds unnecessary complexity. You would need to orchestrate multiple jobs and workflows, which requires more setup and development compared to using DataBrew for the transformations.


NEW QUESTION # 38
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?

Answer: B


NEW QUESTION # 39
To troubleshoot data load failure in one of your Copy Statement, Data Engineer have Executed a COPY statement with the VALIDATION_MODE copy option set to RETURN_ALL_ERRORS with reference to the set of files he had attempted to load. Which below function can facilitate analysis of the problematic records on top of the Results produced? [Select 2]

Answer: C,D

Explanation:
Explanation
LAST_QUERY_ID() Function
Returns the ID of a specified query in the current session. If no query is specified, the most recently executed query is returned.
RESULT_SCAN() Function
Returns the result set of a previous command (within 24 hours of when you executed the query) as if the result was a table.
The following example validates a set of files (SFfile.csv.gz) that contain errors. To facilitate analy-sis of the errors, a COPY INTO <location> statement then unloads the problematic records into a text file so they could be analyzed and fixed in the original data files. The statement queries the RESULT_SCAN table.
1.#copy into Snowtable
2.from @SFstage/SFfile.csv.gz
3.validation_mode=return_all_errors;
4.#set qid=last_query_id();
5.#copy into @SFstage/errors/load_errors.txt from (select rejected_record from ta-ble(result_scan($qid))); Note: Other options are not valid functions.


NEW QUESTION # 40
Which UDF programming language is not supported with Snowflake Secure Data Sharing feature?

Answer: C


NEW QUESTION # 41
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling-queues.html


NEW QUESTION # 42
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