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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Performance Optimization	- Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms - Warehouse optimization 1. Concurrency management 2. Scaling strategies
Data Engineering Fundamentals on Snowflake	- Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies - Snowflake architecture for data engineering 1. Virtual warehouses and compute scaling 2. Storage and compute separation
Security, Governance, and Data Sharing	- Access control 1. Object-level permissions 2. Role-based access control (RBAC) - Data governance 1. Secure data sharing concepts 2. Data masking policies
Semi-Structured and Advanced Data Handling	- Advanced transformation techniques 1. Flattening nested data 2. Complex transformations in SQL - Semi-structured data processing 1. JSON, Avro, Parquet handling 2. VARIANT data type usage
Data Pipelines and Transformation	- ETL/ELT design patterns in Snowflake 1. Data orchestration concepts 2. Staging and transformation layers - Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks
Data Loading and Integration	- Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows - Bulk data loading 1. COPY INTO command usage 2. File format handling

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
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. 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.
B. 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.
C. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
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: C

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 # 247
A data engineer is building a data orchestration workflow. The data engineer plans to use a hybrid model that includes some on-premises resources and some resources that are in the cloud. The data engineer wants to prioritize portability and open source resources.
Which service should the data engineer use in both the on-premises environment and the cloud- based environment?

A. Amazon Simple Workflow Service (Amazon SWF)
B. AWS Data Exchange
C. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
D. AWS Glue

Answer: C

Explanation:
Amazon MWAA is a managed service for Apache Airflow, which is an open-source workflow automation tool. Apache Airflow can be used both on-premises and in the cloud, making it ideal for hybrid environments. Using Amazon MWAA allows the data engineer to leverage the managed service in the cloud while maintaining the ability to use the same open-source Airflow setup on-premises, ensuring portability and consistency across environments.

NEW QUESTION # 248
A lab uses IoT sensors to monitor humidity, temperature, and pressure for a project. The sensors send 100 KB of data every 10 seconds. A downstream process will read the data from an Amazon S3 bucket every 30 seconds.
Which solution will deliver the data to the S3 bucket with the LEAST latency?

A. Use Amazon Kinesis Data Streams and call the Kinesis Client Library to deliver the data to the S3 bucket. Use a 5 second buffer interval from an application.
B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) and Amazon Kinesis Data Firehose to deliver the data to the S3 bucket. Use a 5 second buffer interval for Kinesis Data Firehose.
C. Use Amazon Kinesis Data Streams to deliver the data to the S3 bucket. Configure the stream to use 5 provisioned shards.
D. Use Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose to deliver the data to the S3 bucket. Use the default buffer interval for Kinesis Data Firehose.

Answer: B

NEW QUESTION # 249
A Data Engineer is building a set of reporting tables to analyze consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing Which views are needed MINIMALLY as data sources?

A. SNOWFLAKE.DATA_SHARING_USAGE.LISTING_CONSOKE>TION_DAILY
B. SNOWFLAKE. DATA_SHARING_USAGE. LISTING_TELEMETRY_DAILy
C. SNOWFLAKE.ACCOUNT_USAGE.DATA _TRANSFER_HISTORY
D. SNOWFLAKE- DATA_SHARING_USAGE - LISTING_EVENTS_BAILY

Answer: A

Explanation:
Explanation
The SNOWFLAKE.DATA SHARING _USAGE.LISTING_CONSOKE>TION_DAILY view provides information about consumer requests by region for each of the Data Exchange offeringsannually, as well as click-through rates for each listing. This view is the minimal data source needed for building the reporting tables. The other views are not relevant for this use case.

NEW QUESTION # 250
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 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.
C. 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.
D. 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.

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 # 251
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