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Snowflake DEA-C01 Exam Syllabus Topics:

Topic
Details

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

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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time- based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.
B. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

Answer: D

NEW QUESTION # 74
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
B. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
C. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
D. Specify a combination of distribution, sort, and partition keys for all tables.

Answer: B

Explanation:
Use the ALL Distribution Style for Rarely Updated Small Tables. This approach optimizes the performance of joins involving these smaller tables and is a common best practice in Redshift data warehousing. For the larger tables, maintaining the EVEN distribution style or considering a KEY-based distribution (if there are common join columns) could be more appropriate.

NEW QUESTION # 75
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance. Which solution will meet these requirements?

A. Specify the smaller table on the left side of the join and the larger table on the right side of the join.
B. Specify the larger table on the left side of the join and the smaller table on the right side of the join.
C. Use table statistics to automatically determine the join order.
D. Use AWS Glue to pre-process the tables before performing the join.

Answer: A

Explanation:
Amazon Athena (based on Presto/Trino) performs joins more efficiently when the smaller table is placed on the left side because it is used as the build side of the join and can be loaded into memory. This minimizes data shuffling and improves performance when joining with a larger table on the right side.

NEW QUESTION # 76
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?

A. Data Hub
B. Snowflake Schema
C. Star Schema
D. Data Vault

Answer: D

Explanation:
Explanation
In Data Vault modelling, Hubs are entities of interest to the business.
They contain just a distinct list of business keys and metadata about when each key was first loaded and from where.
In Data Vault modelling, Satellites connect to Hubs or Links. They are Point in Time: so we can ask and answer the question, "what did we know when?" Satellites contain data about their parent Hub or Link and metadata about when the data was load-ed, from where, and a business effectivity date.

NEW QUESTION # 77
The JSON below is stored in a variant column named v in a table named jCustRaw:

Which query will return one row per team member (stored in the teamMembers array) along all of the attributes of each team member?

Answer: C

NEW QUESTION # 78
......

The SnowPro Advanced: Data Engineer Certification Exam (DEA-C01) certification is the way to go in the modern Snowflake era. Success in the Snowflake DEA-C01 exam of this certification plays an essential role in an individual's future growth. Nowadays, almost every tech aspirant is taking the test to get Snowflake DEA-C01 Certification and find well-paying jobs or promotions. But the main issue that most of the candidates face is not finding updated Snowflake DEA-C01 practice questions to prepare successfully for the Snowflake DEA-C01 certification exam in a short time.

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