100％合格率DEA-C01試題＆認證考試的領導者材料和真實的DEA-C01考試

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Snowflake DEA-C01 考試大綱：

主題簡介
主題 1
  • 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.
主題 2
  • 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.
主題 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.
主題 4
  • 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.
主題 5
  • 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.

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最新的 SnowPro Advanced DEA-C01 免費考試真題 (Q108-Q113):

問題 #108
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

答案：A

解題說明：
Data Exchange is the AWS official third-party datasets repository:
https://aws.amazon.com/data-exchange


問題 #109
A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.

答案：C

解題說明：
By defining and enforcing a strict schema in PySpark that specifies the exact format you expect for postal codes (for example, a string of five digits), Spark will automatically flag or coerce any values that don't match. Passing that schema into your Glue DynamicFrame ensures that malformed postal codes are identified and can be corrected or filtered out as part of your ETL logic, eliminating the downstream errors.


問題 #110
Mark the incorrect statement when Data Engineer implement Automating Continuous Data Loading Using Cloud Messaging?

答案：C

解題說明：
Explanation
Triggering automated Snowpipe data loads using S3 event messages is supported by Snowflake ac-counts hosted on Amazon Web Services (AWS) only.
Rest is correct statements.


問題 #111
When would a Data engineer use table with the flatten function instead of the lateral flatten combination?

答案：B

解題說明：
Explanation
The TABLE function with the FLATTEN function is used to flatten semi-structured data, such as JSON or XML, into a relational format. The TABLE function returns a table expression that can be used in the FROM clause of a query. The TABLE function with the FLATTEN function requires another source in the FROM clause to refer to, such as a table, view, or subquery that contains the semi-structured data. For example:
SELECT t.value:city::string AS city, f.value AS population FROM cities t, TABLE(FLATTEN(input => t.value:population)) f; In this example, the TABLE function with the FLATTEN function refers to the cities table in the FROM clause, which contains JSON data in a variant column named value. The FLATTEN function flattens the population array within each JSON object and returns a table expression with two columns: key and value.
The query then selects the city and population values from the table expression.


問題 #112
A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.
The company wants to transform the data to optimize query runtime and storage costs.
Which file format and compression solution will meet these requirements for Athena queries?

答案：B

解題說明：
Parquet provides efficient columnar storage, enabling Athena to read only the necessary data for queries, which reduces scan times and speeds up query performance.
Snappy compression offers a good balance between compression speed and efficiency, reducing storage costs without significantly impacting query times.


問題 #113
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