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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
  • 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
  • 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 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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
To help manage STAGE storage costs, Data engineer recommended to monitor stage files and re-move them from the stages once the data has been loaded and the files which are no longer needed. Which option he can choose to remove these files either during data loading or afterwards?

Answer: B,C

Explanation:
Explanation
Managing Data Files
Staged files can be deleted from a Snowflake stage (user stage, table stage, or named stage) using the following methods:
Files that were loaded successfully can be deleted from the stage during a load by specifying the PURGE copy option in the COPY INTO <table> command.
After the load completes, use the REMOVE command to remove the files in the stage.
Removing files ensures they aren't inadvertently loaded again. It also improves load performance, because it reduces the number of files that COPY commands must scan to verify whether existing files in a stage were loaded already.


NEW QUESTION # 71
A data engineer needs to optimize the performance of a data pipeline that handles retail orders.
Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
Which solution will meet this requirement MOST cost-effectively?

Answer: D

Explanation:
Partitioning the S3 data by the order date lets Athena prune scans to only the relevant date folders, keeping query times stable as data grows. Because Athena is a serverless, pay-per- query service, you only pay for the data scanned, making it the most cost-effective way to run your weekly date-range metrics.


NEW QUESTION # 72
If you need to connect to Snowflake using a BI tool or technology, which of the following BI tools and technologies are known to provide native connectivity to Snowflake?

Answer: B

Explanation:
Explanation
SISENSE is BI tools and technologies which is known to provide native connectivity to Snowflake, Rest of the options given are security & governance tools supported by SnowFlake.
Business intelligence (BI) tools enable analyzing, discovering, and reporting on data to help execu-tives and managers make more informed business decisions. A key component of any BI tool is the ability to deliver data visualization through dashboards, charts, and other graphical output.
For More details around supported BI Tools in Snowflake Ecosystem, do refer the link below:
https://docs.snowflake.com/en/user-guide/ecosystem-bi


NEW QUESTION # 73
For evolving schema and high compatibility, which data format should be chosen for downstream analytics?

Answer: B


NEW QUESTION # 74
A company uses AWS Glue Apache Spark jobs to handle extract, transform, and load (ETL) workloads. The company has enabled logging and monitoring for all AWS Glue jobs.
One of the AWS Glue jobs begins to fail. A data engineer investigates the error and wants to examine metrics for all individual stages within the job.
How can the data engineer access the stage metrics?

Answer: A

Explanation:
AWS Glue provides a built-in Spark UI (accessible from the Glue console's job run details) that exposes per-stage metrics, such as task counts, shuffle read/write sizes, and execution times, for each Spark stage. This is the most direct way to drill down into the individual stages when troubleshooting a failing Glue Spark job.


NEW QUESTION # 75
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