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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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	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	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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q235-Q240):

NEW QUESTION # 235
A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline.
The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow. Which solutions will meet these requirements? (Choose two.)

A. Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
B. Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
C. Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
D. Provision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined schedule
E. Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.

Answer: C,E

Explanation:
AWS Step Functions is a serverless orchestration service that can coordinate multi-step ETL workflows, invoke AWS Glue jobs, manage dependencies, and provide built-in retry and error- handling logic. This makes it a strong fit for orchestrating a serverless ETL pipeline end to end.
AWS Glue workflows are specifically designed to orchestrate AWS Glue jobs, crawlers, and triggers in a dependency-based ETL graph. They provide a native way to represent and manage task order and workflow execution for serverless Glue-based pipelines.

NEW QUESTION # 236
A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
Which conditions must be met? (Select THREE).

A. The query must be executed using the same virtual warehouse.
B. The table structure contributing to the query result cannot have changed
C. The new query must have the same syntax as the previously executed query.
D. The micro-partitions cannot have changed due to changes to other data in the table
E. The results must be reused within 72 hours.
F. The USED_CACHED_RESULT parameter must be included in the query.

Answer: B,C,E

Explanation:
Explanation
Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.

NEW QUESTION # 237
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?

A. Split larger files into a greater number of smaller files, maximize the processing over-head for each file.
(Correct)
B. if the "null" values in your files indicate missing values and have no other special mean-ing, Snowflake recommend setting the file format option STRIP_NULL_VALUES to TRUE when loading the semi-structured data file.
C. Amazon Kinesis Firehose can be convenient way to aggregate and batch data files which also allows defining both the desired file size, called the buffer size, and the wait interval after which a new file is sent, called the buffer interval.
D. Enabling the STRIP_OUTER_ARRAY file format option for the COPY INTO <ta-ble> command to remove the outer array structure and load the records into separate table rows.
E. When preparing your delimited text (CSV) files for loading, the number of columns in each row should be consistent.

Answer: A

Explanation:
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.

NEW QUESTION # 238
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics. Which solution will meet these requirements with the LEAST ongoing maintenance?

A. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.
B. Use Amazon Data Firehose to ingest the streaming data. Deliver the processed data directly to a provisioned Amazon Redshift cluster.
C. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) broker to collect the data.Store the data in an Amazon RDS DB instance.
D. Deploy agents on Amazon EC2 instances to collect the streaming data. Use the AWS CLI to periodically batch upload the data to an Amazon S3 bucket.

Answer: A

Explanation:
Amazon Kinesis Data Streams is well suited to collect high-volume real-time clickstream data from websites and mobile apps, and Amazon Data Firehose can then deliver that streaming data to Amazon S3 with very little operational effort. This approach is highly managed and avoids the ongoing administration required for EC2-based collectors, self-managed Kafka patterns, or maintaining a provisioned Redshift cluster for raw landing storage.
Reference:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/firehose/latest/dev/basic-deliver.html

NEW QUESTION # 239
A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk in near real time for further analysis.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the delivery stream.
B. Configure an Amazon Kinesis Data Streams data stream to use Splunk as the destination. Create a CloudWatch Logs subscription filter to send log events to the data stream.
C. Create an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination. Create a CloudWatch Logs subscription filter to send log events to the delivery stream.
D. Configure an Amazon Kinesis Data Streams data stream to use Splunk as the destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the data stream.

Answer: C

Explanation:
Kinesis Data Firehose has built-in support for Splunk as a destination, making the integration straightforward. Using a CloudWatch Logs subscription filter directly to Firehose simplifies the data flow, eliminating the need for additional Lambda functions or custom integrations.

NEW QUESTION # 240
......

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