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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q280-Q285):

NEW QUESTION # 280
A data engineer needs to validate the quality of files that are uploaded to an Amazon S3 bucket every day. The files are in CSV and JSON formats and schema variations exist. The data engineer needs a repeatable process to monitor data quality metrics such as null values, format inconsistencies, and outliers. The process must provide reusable rule-based profiling and scale across multiple datasets with minimal manual effort or custom code.
Which solution will meet these requirements?

A. Use AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
B. Use AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
C. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.
D. Use AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.

Answer: C

Explanation:
AWS Glue DataBrew is designed for low-code data profiling and data quality validation across datasets stored in Amazon S3. It can generate profile statistics such as null values and outliers, and it supports reusable data quality rules and repeatable jobs, which matches the requirement for a scalable, low-effort process across CSV and JSON datasets with schema variation. Manual dashboard review alone would not provide the repeatable rule-based validation the question requires.
Reference:
https://docs.aws.amazon.com/databrew/latest/dg/profile.data-quality-rules.html
https://docs.aws.amazon.com/databrew/latest/dg/getting-started.02.html

NEW QUESTION # 281
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.

A. Third-party supported Python packages are locked down to prevent hitting
B. Python packages can only be loaded in a local environment
C. The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
D. Python packages can access any external endpoints
E. Querying information__schema .packages will provide a list of supported Python packages and versions

Answer: C,D,E

Explanation:
Explanation
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.

NEW QUESTION # 282
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.
B. Use an S3 bucket that is in the same account that uses Athena to query the data.
C. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
D. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

Answer: D,E

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html

NEW QUESTION # 283
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 DriverID as the partition key and RideStatus as the sort key.
C. Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
D. Create a local secondary index (LSI) on DriverID.

Answer: B

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 # 284
The JSON below is stored in a variant column named v in a table named jCustRaw:

Which query will return one row per team member (stored in the teamMembers array) along all of the attributes of each team member?

Answer: B

NEW QUESTION # 285
......

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