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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.
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
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q342-Q347):

NEW QUESTION # 342
A Data Engineer has created table t1 with datatype VARIANT:
create or replace table t1 (cl variant);
The Engineer has loaded the following JSON data set. which has information about 4 laptop models into the table:

The Engineer now wants to query that data set so that results are shown as normal structured data. The result should be 4 rows and 4 columns without the double quotes surrounding the data elements in the JSON data.
The result should be similar to the use case where the data was selected from a normal relational table z2 where t2 has string data type columns model__id. model, manufacturer, and =iccisi_r.an=. and is queried with the SQL clause select * from t2; Which select command will produce the correct results?

Answer: D


NEW QUESTION # 343
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 # 344
The COPY command supports several options for loading data files from a stage i.e.
I. By path
II. Specifying a list of specific files to load.
III. Using pattern matching to identify specific files by pattern.
IV. Organize files into logical paths that reflect a scheduling pattern.
Of the aforesaid options for identifying/specifying data files to load from a stage, which option in general is the fastest & best considerate?

Answer: C

Explanation:
Explanation
Of the above options for identifying/specifying data files to load from a stage, providing a discrete list of files is generally the fastest; however, the FILES parameter supports a maximum of 1,000 files, meaning a COPY command executed with the FILES parameter can only load up to 1,000 files.
For example:
copy into load1 from @%load1/Snow1/ files=('mydata1.csv', 'mydata2.csv', 'mydata3.csv')


NEW QUESTION # 345
A company is building a dashboard for thousands of Analysts. The dashboard presents the results of a few summary queries on tables that are regularly updated. The query conditions vary by tope according to what data each Analyst needs Responsiveness of the dashboard queries is a top priority, and the data cache should be preserved.
How should the Data Engineer configure the compute resources to support this dashboard?

Answer: B

Explanation:
Explanation
This option is the best way to configure the compute resources to support this dashboard. By assigning all queries to a multi-cluster virtual warehouse set to maximized mode, the Data Engineer can ensure that there is enough compute capacity to handle thousands of concurrent queries from different analysts. A multi-cluster virtual warehouse can scale up or down by adding or removing clusters based on the load. A maximized scaling policy ensures that there is always at least one cluster running and that new clusters are added as soon as possible whenneeded. By monitoring the utilization and performance of the virtual warehouse, the Data Engineer can determine the smallest suitable number of clusters that can meet the responsiveness requirement and minimize costs.


NEW QUESTION # 346
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?

Answer: B

Explanation:
AWS Glue Python Shell Job is billed $0.44 per DPU-Hour for each job.
AWS Glue PySpark is billed $0.29 per DPU-Hour for each job with flexible execution and $0.44 per DPU-Hour for each job with standard execution.
https://aws.amazon.com/glue/pricing/


NEW QUESTION # 347
......

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