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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
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. Batch loading strategies 2. Continuous ingestion concepts
Performance Optimization	- Query performance tuning 1. Caching mechanisms 2. Clustering and micro-partitions - Warehouse optimization 1. Concurrency management 2. Scaling strategies
Security, Governance, and Data Sharing	- Data governance 1. Secure data sharing concepts 2. Data masking policies - Access control 1. Role-based access control (RBAC) 2. Object-level permissions
Data Pipelines and Transformation	- ETL/ELT design patterns in Snowflake 1. Staging and transformation layers 2. Data orchestration concepts - Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks
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
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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q314-Q319):

NEW QUESTION # 314
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_TELEMETRY_DAILy
B. SNOWFLAKE.ACCOUNT_USAGE.DATA _TRANSFER_HISTORY
C. SNOWFLAKE.DATA_SHARING_USAGE.LISTING_CONSOKE>TION_DAILY
D. SNOWFLAKE- DATA_SHARING_USAGE - LISTING_EVENTS_BAILY

Answer: C

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 # 315
A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions. Which solution will meet this requirement?

A. Configure an S3 bucket policy rule that denies the creation of S3 delete markers.
B. Set up AWS Config remediation actions to prevent users from deleting S3 objects.
C. Create an S3 Lifecycle rule that moves deleted files to S3 Glacier Deep Archive.
D. Enable versioning on the S3 bucket and configure MFA delete.

Answer: D

Explanation:
Enabling S3 versioning preserves all object versions so data can be recovered if deletion occurs, and MFA delete adds an additional layer of protection by requiring multi-factor authentication to permanently delete objects or change versioning settings, effectively preventing accidental deletions.

NEW QUESTION # 316
A data engineer needs to deploy a serverless data pipeline. In the pipeline, CSV files are uploaded to an Amazon S3 bucket, which invokes an AWS Lambda function. The Lambda function transforms the CSV files to JSON format and stores the results in a second S3 bucket.
The data engineer has created an AWS Serverless Application Model (AWS SAM) template that includes the Lambda function. The data engineer wants to use AWS SAM for the pipeline deployment.
Which solution will package and deploy this serverless data pipeline?

A. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command to prepare the deployment package. Run the sam deploy --guided command to deploy the pipeline.
B. Add the first S3 bucket to the SAM template. Run the sam package template to upload the Lambda function code to Amazon S3. Create an AWS CloudFormation stack from the packaged template. Configure event notifications manually.
C. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command followed by the aws cloudformation deploy command to deploy the pipeline.
D. Run the sam deploy command directly with the --s3-bucket parameter to deploy the Lambda function code. Manually configure the S3 event trigger in the AWS Management Console.

Answer: A

Explanation:
AWS SAM can define both the Amazon S3 bucket and the S3 event source that invokes the Lambda function directly in the SAM template. The standard deployment flow is to run sam build to prepare the application artifacts and then run sam deploy --guided to package and deploy the serverless resources through AWS CloudFormation. This provides a complete infrastructure-as- code deployment for the pipeline without requiring manual trigger configuration.

NEW QUESTION # 317
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

A. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.
B. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
C. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.
D. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Answer: C

Explanation:
AWS CloudTrail provides detailed logging of AWS API calls, including object-level events for Amazon S3. You can configure CloudTrail to track data events for the S3 bucket, which will log each access to the objects in the bucket, including the username of the IAM entity (user or role) that accessed the data. By forwarding these CloudTrail events to Amazon CloudWatch, you can set up alarms to trigger notifications when policy violations are detected, and include the violating user's identity.
AWS Config is primarily designed for monitoring resource configurations, not for tracking real- time access events like data access violations. It does not directly provide information about which user accessed the S3 objects.
CloudWatch metrics can monitor S3 storage or request metrics, but it does not provide detailed logging of object-level access, including the username of the violator. You need CloudTrail to get detailed event information.
While S3 server access logs can track access to objects, they lack real-time processing and do not provide the same level of detail as CloudTrail.
Additionally, processing access logs with CloudWatch metric filters adds more complexity compared to using CloudTrail, which is designed for this use case.

NEW QUESTION # 318
A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day.
The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
B. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
C. Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS.Use Amazon Kendra for full-text search capabilities.
D. Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.

Answer: B

Explanation:
Amazon OpenSearch Service is the AWS service in these choices that is purpose-built for complex full-text search of legal documents. AWS Glue can run ETL jobs, and AWS Step Functions can orchestrate both time-based and event-based processing so the daily legal document refresh and the frequent lawyer availability updates can be handled in one workflow pattern. This lets the company keep lawyer availability current within the required 5-minute window while using a managed search service and avoiding custom search implementation.

NEW QUESTION # 319
......

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