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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 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.
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
  • 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q210-Q215):

NEW QUESTION # 210
A media company wants to use Amazon OpenSearch Service to analyze rea-time data about popular musical artists and songs. The company expects to ingest millions of new data events every day. The new data events will arrive through an Amazon Kinesis data stream. The company must transform the data and then ingest the data into the OpenSearch Service domain.
Which method should the company use to ingest the data with the LEAST operational overhead?

Answer: D

Explanation:
Amazon Kinesis Data Firehose is a fully managed service that reliably loads streaming data into data lakes, data stores and analytics services like OpenSearch Service. It can automatically scale to match the throughput of your data and requires no ongoing administration.


NEW QUESTION # 211
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.


NEW QUESTION # 212
Snowflake does not treat the inner transaction as nested; instead, the inner transaction is a separate transaction.
What is term used to call these Transaction?

Answer: E


NEW QUESTION # 213
A company utilizes AWS Glue for ETL (Extract, Transform, Load) jobs to process large volumes of data stored in Amazon S3. Recently, they have been experiencing performance issues with their Glue jobs, resulting in longer processing times and delays in data delivery. Upon investigation, they found that the bottleneck occurred during data extraction from S3. Which of the following could be the potential cause of this performance issue?

Answer: D

Explanation:
The bottleneck occurs during data extraction from S3, indicating a potential issue with the Glue job's configuration in this scenario. If the job is not optimized for parallelism, it may not efficiently utilize available resources, leading to slower data extraction. Optimizing the Glue job's configuration to enable parallel processing can improve the efficiency of data extraction from S3, reducing processing times.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/optimize-spark-etl-job-performance.html


NEW QUESTION # 214
Data Engineer try to load data from external stage using Snowpipe & later find out that some Set of Files Not Loaded. To debug the issue, she used COPY_HISTORY function & cross verified that its output indicates a subset of files was not loaded. What is possible reason of arising this situation in both REST API call and Auto-Ingest methods? [Select 2]

Answer: B,C

Explanation:
Explanation
COPY_HISTORY Record Indicates Unloaded Subset of Files:
If the COPY_HISTORY function output indicates a subset of files was not loaded, you may try to "refresh" the pipe.
This situation can arise in any of the following situations:
The external stage was previously used to bulk load data using the COPY INTO table command.
REST API:
o External event-driven functionality is used to call the REST APIs, and a backlog of data files al-ready existed in the external stage before the events were configured.
Auto-ingest:
o A backlog of data files already existed in the external stage before event notifications were con-figured.
o An event notification failure prevented a set of files from getting queued.
To load the data files in your external stage using the configured pipe, execute an ALTER PIPE ... REFRESH statement.


NEW QUESTION # 215
......

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