Quiz Snowflake - DEA-C01–High Pass-Rate Reliable Dumps Sheet

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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
  • 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.
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
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
Mohan, Data engineer works with ALTUSO Company, wants to programmatically check the status of the query. He needs query id to identify each query executed by Snowflake & using Snowflake Connector for Python to execute a query, how he will be able to meet this requirements. Select the best options you will suggest?

Answer: C

Explanation:
Explanation
Retrieving the Snowflake Query ID
A query ID identifies each query executed by Snowflake. When you use the Snowflake Connector for Python to execute a query, you can access the query ID through the sfqid attribute in the Cursor object:
1.# Retrieving a Snowflake Query ID
2.cur = con.cursor()
3.cur.execute("SELECT * FROM testtable")
4.print(cur.sfqid)


NEW QUESTION # 205
A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A


NEW QUESTION # 206
A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company's data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region.
A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.
Which solution will meet these requirements with the LEAST effort?

Answer: C

Explanation:
AWS DataSync is designed for transferring data between AWS storage services across regions, such as EFS, S3, and other storage systems. By using DataSync, you can efficiently transfer data from each of the Regional EFS file systems to the EFS file system in the data science team's Region.
This allows the data to remain within the data science team's Region while being processed, which meets the requirement that data cannot leave the Region.
Once the data is transferred, Lambda functions in the same Region as the data science team can mount the local EFS file system and process the data, minimizing complexity and the risk of network-related latency or failures from cross-region data processing.
This approach ensures minimal operational effort because DataSync automates the transfer process and works seamlessly with EFS, while keeping the data localized for processing by Lambda functions in the same Region.


NEW QUESTION # 207
What is the primary purpose of data lineage in data engineering?

Answer: C


NEW QUESTION # 208
A company is developing a log streaming pipeline that uses Amazon Data Firehose. The pipeline streams Amazon CloudWatch Logs data to an Amazon S3 bucket. The company's analytics team needs to use the data in audits. The pipeline must deliver only the relevant logs to the S3 bucket in a compatible format for the team's analysis. Which solution will meet these requirements and maintain reliable performance?

Answer: B

Explanation:
A CloudWatch Logs subscription filter can send only the relevant log events from the log group to an Amazon Data Firehose delivery stream. Firehose can then invoke an AWS Lambda function to transform the records into the format the analytics team needs before delivering them to Amazon S3. This approach is managed, scalable, and reliable for continuous log delivery.


NEW QUESTION # 209
......

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