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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 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.
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	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	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q177-Q182):

NEW QUESTION # 177
Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]

A. Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
B. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.
C. Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.
D. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
E. Snowflake supports resizing a warehouse at any time, even while running.

Answer: A,B,C,D,E

Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
Snowflake supports resizing a warehouse at any time, even while running. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following:
Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing.
Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.

NEW QUESTION # 178
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

A. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access.Restrict access based on Region.
B. Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.
C. Modify the IAM roles of the HR departments to add a data filter for each department's Region.
D. Use data filters for each Region to register the S3 paths as data locations.
E. Register the S3 path as an AWS Lake Formation location.

Answer: B,E

Explanation:
https://docs.aws.amazon.com/lake-formation/latest/dg/data-filters-about.html
https://docs.aws.amazon.com/lake-formation/latest/dg/access-control-fine-grained.html

NEW QUESTION # 179
Select the incorrect statement while working with warehouses?

A. Resizing a suspended warehouse does not provision any new compute resources for the warehouse.
B. Compute resources waiting to shut down are considered to be in "quiesce" mode.
C. Resizing a warehouse will have any immediate impact on statements that are currently being executed by the warehouse.
D. Resizing a warehouse to a larger size is useful while loading and unloading significant amounts of data.

Answer: C

Explanation:
Explanation
Resizing a warehouse doesn't have any impact on statements that are currently being executed by the warehouse. When resizing to a larger size, the new compute resources, once fully provisioned, are used only to execute statements that are already in the warehouse queue, as well as all future statements submitted to the warehouse.

NEW QUESTION # 180
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]

A. A backlog of data files already existed in the external stage do not have any impact on Load failure, as this is well managed by serverless SnowPipe
B. Files modified and staged again after 14 days and Snowpipe ignores modified files that are staged again.
C. An event notification failure prevented a set of files from getting queued.
D. External event-driven functionality is used to call the REST APIs, and a backlog of da-ta files already existed in the external stage before the events were configured.

Answer: C,D

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 # 181
A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database.
The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead.
Which solution will meet these requirements?

A. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
B. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.
C. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
D. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.

Answer: A

Explanation:
AWS DMS with ongoing replication (CDC) continuously streams new RDS for MySQL transactions into Amazon Redshift, providing near real-time freshness with minimal maintenance and transferring only changes instead of full batches. Reports can then run in Redshift joining current and historical data without hourly ETL jobs or S3 staging.

NEW QUESTION # 182
......

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