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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: Data Loading and Integration	- Bulk data loading 1. COPY INTO command usage 2. File format handling - Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows
Topic 3: Performance Optimization	- Warehouse optimization 1. Scaling strategies 2. Concurrency management - Query performance tuning 1. Caching mechanisms 2. Clustering and micro-partitions
Topic 4: Semi-Structured and Advanced Data Handling	- Semi-structured data processing 1. JSON, Avro, Parquet handling 2. VARIANT data type usage - Advanced transformation techniques 1. Complex transformations in SQL 2. Flattening nested data
Topic 5: Data Pipelines and Transformation	- Streams and Tasks 1. Automating pipelines with Tasks 2. Change Data Capture (CDC) with Streams - ETL/ELT design patterns in Snowflake 1. Staging and transformation layers 2. Data orchestration concepts
Topic 6: Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Virtual warehouses and compute scaling 2. Storage and compute separation - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
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 a CloudWatch Logs subscription filter to send log events to the delivery stream.
B. 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.
C. 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.
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: A

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 # 94
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:

The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?

A. Create a filter expression that uses RiderID and RideStatus.
B. Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
C. Create a local secondary index (LSI) on DriverID.
D. Create a global secondary index (GSI) that uses DriverID as the partition key and RideStatus as the sort key.

Answer: D

Explanation:
To let drivers efficiently query only their completed rides, you need a global secondary index (GSI) with DriverID as the partition key (so queries can be targeted per driver) and RideStatus as the sort key (so you can query for "Completed" rides without scanning the full table). This avoids costly scans and supports fast, targeted lookups at scale.

NEW QUESTION # 95
A company uses Amazon SageMaker AI for its machine learning (ML) workflows. The company is organized into several project groups that use sensitive data. The company needs to give the project groups the ability to discover available datasets across different AWS accounts. The solution must maintain access controls and track all data access for compliance purposes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Set up IAM roles for each project group with permissions to access all datasets across all AWS accounts. Use AWS CloudTrail to record data access activity.
B. Create separate Amazon SageMaker Studio domains for each project group with isolated environments and no ability to share data between domains.
C. Set up Amazon SageMaker Feature Store with cross-account access policies to automatically share data between AWS accounts without requiring approval workflows.
D. Use Amazon SageMaker Assets to publish, discover, and request access to datasets through the asset catalog with approval workflows that track data access.

Answer: D

Explanation:
Amazon SageMaker Assets (via SageMaker Catalog) provides a centralized way to publish and discover datasets across accounts with built-in governance. It supports approval workflows to control access and automatically tracks dataset usage for compliance, meeting the requirements with minimal operational overhead while maintaining proper access controls.

NEW QUESTION # 96
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 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.
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 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.
D. 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.

Answer: D

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 # 97
As Data Engineer, you have been asked to access data held in AWS Glacier Deep Archive storage class for Historical Data Analysis, which one is the correct statement to recommend?

A. Loading data from AWS cloud storage services is supported regardless of the cloud platform that hosts your Snowflake account.
B. Upload (i.e. stage) files to your cloud storage account using the tools provided by the cloud storage service.
C. We can simply access AWS Glacier Deep Archive storage External Stage data using PUT command.
D. You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved.
E. Data can be accessed from External stage using AWS Private link in this case.

Answer: D

Explanation:
Explanation
External stage
References data files stored in a location outside of Snowflake. Currently, the following cloud stor-age services are supported:
Amazon S3 buckets
Google Cloud Storage buckets
Microsoft Azure containers
The storage location can be either private/protected or public.
You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved. These archival storage classes include, for example, the Amazon S3 Glacier Flexible Retrieval or Glacier Deep Archive storage class, or Microsoft Azure Archive Storage.

NEW QUESTION # 98
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