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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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 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	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 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 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 (Q182-Q187):

NEW QUESTION # 182
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?

A. Files no longer needed, can be removed using the PURGE=TRUE command.
B. Files no longer needed, can be removed using the REMOVE command.
C. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
D. Script can be used during data loading & post data loading with DELETE command.

Answer: A,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 # 183
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

A. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.
B. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
C. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
D. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis

Answer: B

Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.

NEW QUESTION # 184
Pivoting in SQL is mainly used to transform data from:

A. Multiple rows into multiple columns
B. One row to one column
C. Only one column to one row
D. Multiple columns into multiple rows

Answer: A

NEW QUESTION # 185
A media company wants to build a real-time analytics pipeline to process customer activity events across the company's website and mobile app. The company wants to build a solution to ingest millions of events with minimum latency. The solution must be scalable and durable enough so that no data is lost. Which solution will meet these requirements in the MOST cost-effective way?

A. Schedule an AWS Glue job to fetch user interaction logs every 10 minutes from Amazon S3.
Configure the AWS Glue job to transform and store the data in Amazon Redshift for analytics.
B. Set up an Amazon Kinesis Data Streams pipeline to ingest data, process the data by using AWS Lambda functions, and store the results in Amazon Redshift for analytics.
C. Deploy an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. Use self- managed consumers to process and distribute data in real-time. Integrate with Amazon Redshift for enhanced analytics.
D. Configure Amazon S3 Event Notifications to invoke an AWS Lambda function to process every new interaction log file. Store the result in Amazon Redshift for analytics.

Answer: B

Explanation:
Amazon Kinesis Data Streams is built for durable, scalable, real-time ingestion of streaming events and can process data with very low latency at high volume. AWS documents that Kinesis Data Streams supports real-time stream processing and integrates directly with Amazon Redshift for streaming ingestion, which fits a pipeline handling millions of events without waiting for batch intervals. Using AWS Lambda for stream processing keeps the solution managed and cost- effective, while Amazon MSK would introduce higher cluster cost and operational complexity for this requirement.
Reference:
https://docs.aws.amazon.com/streams/latest/dev/introduction.html

NEW QUESTION # 186
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Choose two.)

A. The AggregationEnabled configuration property was set to true.
B. The max_records configuration property was set to a number that was too high.
C. There was a change in the number of shards, record processors, or both.
D. The stream's value for the IteratorAgeMilliseconds metric was too high.
E. The producer experienced network-related timeouts.

Answer: C,E

NEW QUESTION # 187
......

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