DEA-C01權威認證 - DEA-C01考試心得

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Snowflake DEA-C01 考試大綱：

主題	簡介
主題 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.
主題 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.
主題 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.
主題 4	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 5	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.

>> DEA-C01權威認證 <<

DEA-C01考試心得 & DEA-C01 PDF題庫

長久以來，Snowflake 就是電腦的代名詞。無論在美國國內還是在世界的電腦領域裏，Snowflake 都有著極其深遠的影響。而 DEA-C01 考試是 Snowflake 公司的 SnowPro Advanced: Data Engineer Certification Exam 證照考試官方代號，也是現在最熱門的證照考試，含金量很高。而獲得 Snowflake 的 DEA-C01 證照不僅僅能證明您的IT技術能力，更是您進入職場的敲門磚，也是提高您身價的另一捷徑。

最新的 SnowPro Advanced DEA-C01 免費考試真題 (Q369-Q374):

問題 #369
A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters. Which solution will meet these requirements?

A. Use Amazon Redshift Spectrum to query the data in Amazon S3.
B. Configure query result reuse settings in the Athena workgroup.
C. Convert the dataset to JSON format before running Athena queries.
D. Use Amazon EMR to pre-process the data before running Athena queries.

答案：B

解題說明：
Athena query result reuse allows repeated queries with the same text and parameters to return previously computed results instead of scanning the source data again. This improves performance for repeated queries and reduces query costs by avoiding unnecessary data scans.

問題 #370
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance. Which solution will meet these requirements with the LEAST operational overhead?

A. Set up AWS Database Migration Service (AWS DMS) tasks that perform schema conversions for each database. Load the changes into Amazon Redshift Serverless.
B. Use Aurora zero-ETL integrations with Amazon Redshift Serverless for each database to load Aurora MySQL changes in Amazon Redshift Serverless.
C. Use AWS Database Migration Service (AWS DMS) to set up binary log replication to Amazon Kinesis Data Streams. Load the data into Amazon Redshift Serverless after schema conversion.
D. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Connect with Debezium connectors to load data into Amazon Redshift Serverless.

答案：B

解題說明：
Aurora zero-ETL integration with Amazon Redshift Serverless is the most direct fit because it delivers near real-time data availability for analytics with a fully managed approach and minimal maintenance. It avoids building and operating custom CDC pipelines, streaming infrastructure, or connector frameworks, which gives it the least operational overhead for multiple Aurora MySQL databases.

問題 #371
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?

A. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
B. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage
C. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
D. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table

答案：A

解題說明：
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.

問題 #372
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
B. Store the credentials in a configuration file that is in an Amazon S3 bucket.
C. Grant the AWS Glue job IAM role access to the stored credentials.
D. Store the credentials in the AWS Glue job parameters.
E. Store the credentials in AWS Secrets Manager.

答案：C,E

問題 #373
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date.
The data engineer wants to improve query performance and to automate partition management.
Which solution will meet these requirements?

A. Reduce the number of partitions by changing the partitioning schema from daily to monthly granularity.
B. Increase the processing capacity of Athena queries by allocating more compute resources.
C. Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AWS Glue for each day's data.
D. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

答案：D

解題說明：
Partition projection eliminates the need for frequent metadata lookups or explicit partition registration in the AWS Glue Data Catalog. By defining a date range in your table's properties (for example, projection.enabled=true, projection.order_date.type=DATE, projection.order_date.range=2020/01/01,NOW, and the appropriate format), Athena will calculate partitions on-the-fly. This both speeds up queries (avoiding catalog calls for thousands of partitions) and removes the need for any external partition-management jobs.

問題 #374
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