DEA-C01: SnowPro Advanced: Data Engineer Certification Exam dumps & PassGuide DEA-C01 exam

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q210-Q215):

NEW QUESTION # 210
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?

Answer: B

Explanation:
Defining and creating the calculated field in the dataset ensures that the exchange rate calculations are precomputed before being stored in SPICE (QuickSight's super-fast, parallel, in- memory calculation engine). This allows the calculation to be materialized and reused across different analyses and visuals, ensuring that the precision is maintained and the performance is optimized.
Creating the calculated field in the analysis would compute the result at the analysis level, not within the dataset. This approach doesn't guarantee precomputation within SPICE and may increase the calculation overhead for each analysis.
Defining the field in the visual means the calculation is performed dynamically every time the visual is rendered, which does not leverage SPICE's precomputation benefits and can result in slower performance.
Creating the calculated field at the dashboard level is similar to doing it at the visual level, meaning the calculations are not precomputed and stored in SPICE, leading to potential performance issues and less reuse of the calculation across different analyses or visuals.


NEW QUESTION # 211
Which of the following best describes the type of data found in traditional relational databases?

Answer: B


NEW QUESTION # 212
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-inventory-athena-query.html S3 Select allows you to retrieve a subset of data from an object stored in S3 using simple SQL expressions. It is capable of working directly with objects in Parquet format.


NEW QUESTION # 213
A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?

Answer: C


NEW QUESTION # 214
Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.

Answer: D


NEW QUESTION # 215
......

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