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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 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	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 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 (Q97-Q102):

NEW QUESTION # 97
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. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
B. 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.
C. 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
D. 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.

Answer: A

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 # 98
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Grant the AWS Glue job IAM role access to the stored credentials.
B. Store the credentials in the AWS Glue job parameters.
C. Store the credentials in a configuration file that is in an Amazon S3 bucket.
D. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
E. Store the credentials in AWS Secrets Manager.

Answer: A,E

NEW QUESTION # 99
A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.
The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use S3 Storage Lens standard metrics to determine when to move objects to more cost- optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.
B. Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard- Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.
C. Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.
D. Use S3 Intelligent-Tiering. Use the default access tier.

Answer: D

NEW QUESTION # 100
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

A. External bytes scanned - bytes read from an external object, e.g. a stage.
B. Percentage scanned from cache - the percentage of data scanned from the local disk cache.
C. Bytes written - bytes written (e.g. when loading into a table).
D. Bytes sent over the wireframe - amount of data sent over the wireframe
E. Bytes read from result - bytes read from the result object.

Answer: D

Explanation:
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details

NEW QUESTION # 101
A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.
Which solution will meet these requirements?

A. Create an event-based AWS Glue trigger to start the workflow. Configure Amazon S3 to log events to AWS CloudTrail. Create a rule in Amazon EventBridge to forward PutObject events to the AWS Glue trigger.
B. Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Lambda function that runs every 15 minutes to check the S3 bucket for the daily file. Configure the function to start the AWS Glue workflow if the file is present.
C. Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Database Migration Service (AWS DMS) migration task. Set the DMS source as the S3 bucket. Set the target endpoint as the AWS Glue workflow.
D. Create a scheduled AWS Glue trigger to start the workflow. Create a cron job that runs the AWS Glue job every 15 minutes. Set up the AWS Glue job to check the S3 bucket for the daily file.
Configure the job to stop if the file is not present.

Answer: A

Explanation:
An event-based trigger driven by Amazon EventBridge provides the lowest-latency, lowest- overhead way to kick off your Glue workflow as soon as the file arrives. By enabling S3 data events in CloudTrail and writing a simple EventBridge rule that matches the PutObject event for your bucket, you can target your Glue workflow's trigger directly, eliminating continuous polling or extra Lambda or DMS infrastructure.

NEW QUESTION # 102
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