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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Advanced Snowflake Features	10-15%	- External Integrations 1. Partner integrations (Spark, Kafka, etc.) 2. Snowflake Connector for Python/Java 3. External functions and UDFs - Data Lakes 1. Snowflake as a Data Lake 2. External tables and Iceberg support 3. Querying semi-structured data (VARIANT, ARRAY, OBJECT) - Data Sharing 1. Reader accounts 2. Direct sharing vs Data Marketplace 3. Secure share creation and management
Topic 2: Data Transformation	20-25%	- SQL-Based Transformations 1. Complex JOIN operations and optimizations 2. Merge, update, and delete operations 3. Window functions for advanced analytics 4. Data type conversions and casting - Streams and Tasks 1. Task graph design and dependencies 2. Scheduled and event-based task execution 3. Serverless task warehouse configuration 4. Change Data Capture (CDC) with streams - Data Processing Patterns 1. Zero Copy Cloning for data provisioning 2. Time Travel for historical data access 3. ELT vs ETL approaches in Snowflake
Topic 3: Data Governance and Security	15-20%	- Data Protection 1. External tokenization 2. Data re-identification risks 3. Secure data sharing across accounts 4. Data masking and tokenization - Compliance and Monitoring 1. Tag-based policies 2. Access history and audit trails 3. Data classification - Access Control 1. Role hierarchy and ownership 2. Row-level and column-level security 3. Role-based access control (RBAC) 4. GRANT and REVOKE operations
Topic 4: Data Ingestion	15-20%	- Continuous Data Ingestion 1. Real-time data ingestion patterns 2. Serverless compute for Snowpipe 3. Snowpipe configuration and usage - Bulk Data Loading 1. File format considerations (CSV, JSON, Parquet, Avro) 2. COPY INTO command options and best practices 3. Staging configurations (internal and external stages) 4. Data loading error handling and validation
Topic 5: Performance Engineering	10-15%	- Warehouse Management 1. Resource monitors and cost control 2. Auto-suspend and auto-resume settings 3. Multi-cluster warehouse configuration - Query Optimization 1. Join elimination and simplification 2. Predicate pushdown optimization 3. Query rewrite and optimization techniques
Topic 6: Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Hybrid and Hyper tables 2. Micro-partitioning concepts 3. Clustering strategies and key selection 4. Table types (permanent, transient, temporary) - Performance Optimization 1. Materialized views and cached results 2. Result set caching 3. Query profiling and execution plans 4. Warehouse scaling policies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q228-Q233):

NEW QUESTION # 228
A data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables.
Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?

A. Use the AWS Management Console to edit the rules within the Data Catalog.
B. Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.
C. Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables.
Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.
D. Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.

Answer: B

NEW QUESTION # 229
Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket.
Which solution will meet these requirements?

A. Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.
B. Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.
C. Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.
D. Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

Answer: A

NEW QUESTION # 230
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
B. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
C. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
D. Use the streaming ingestion feature of Amazon Redshift.

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.

NEW QUESTION # 231
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge. All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?

A. Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
B. Use Quick Suite dashboards that have federated query access to Amazon Redshift.
C. Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.
D. Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.

Answer: D

Explanation:
Amazon Q in QuickSight enables users to create dashboards and reports using natural language queries, removing the need for SQL knowledge. It is fully integrated and managed within QuickSight, providing the most operationally efficient way for analysts of varying skill levels to generate insights quickly without additional tooling or infrastructure.

NEW QUESTION # 232
A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.
Which solution will meet this requirement with LEAST operational overhead?

A. Use Amazon Data Firehose and Amazon Comprehend to detect and remove PII.
B. Use S3 Object Lambda to access the data, and use Amazon Comprehend to detect and remove PII.
C. Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.
D. Use Amazon Macie to create and run a sensitive data discovery job to detect and remove PII.

Answer: B

NEW QUESTION # 233
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