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Snowflake DEA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Performance Engineering10-15%- Warehouse Management
  • 1. Auto-suspend and auto-resume settings
  • 2. Resource monitors and cost control
  • 3. Multi-cluster warehouse configuration
- Query Optimization
  • 1. Query rewrite and optimization techniques
  • 2. Join elimination and simplification
  • 3. Predicate pushdown optimization
Data Transformation20-25%- SQL-Based Transformations
  • 1. Merge, update, and delete operations
  • 2. Window functions for advanced analytics
  • 3. Complex JOIN operations and optimizations
  • 4. Data type conversions and casting
- Data Processing Patterns
  • 1. Zero Copy Cloning for data provisioning
  • 2. Time Travel for historical data access
  • 3. ELT vs ETL approaches in Snowflake
- Streams and Tasks
  • 1. Serverless task warehouse configuration
  • 2. Task graph design and dependencies
  • 3. Change Data Capture (CDC) with streams
  • 4. Scheduled and event-based task execution
Data Storage and Retrieval15-20%- Snowflake Architecture
  • 1. Clustering strategies and key selection
  • 2. Table types (permanent, transient, temporary)
  • 3. Micro-partitioning concepts
  • 4. Hybrid and Hyper tables
- Performance Optimization
  • 1. Warehouse scaling policies
  • 2. Query profiling and execution plans
  • 3. Materialized views and cached results
  • 4. Result set caching
Data Governance and Security15-20%- Access Control
  • 1. Role hierarchy and ownership
  • 2. Row-level and column-level security
  • 3. GRANT and REVOKE operations
  • 4. Role-based access control (RBAC)
- Compliance and Monitoring
  • 1. Access history and audit trails
  • 2. Tag-based policies
  • 3. Data classification
- Data Protection
  • 1. Data re-identification risks
  • 2. Data masking and tokenization
  • 3. Secure data sharing across accounts
  • 4. External tokenization
Data Ingestion15-20%- Bulk Data Loading
  • 1. Staging configurations (internal and external stages)
  • 2. COPY INTO command options and best practices
  • 3. File format considerations (CSV, JSON, Parquet, Avro)
  • 4. Data loading error handling and validation
- Continuous Data Ingestion
  • 1. Snowpipe configuration and usage
  • 2. Serverless compute for Snowpipe
  • 3. Real-time data ingestion patterns
Advanced Snowflake Features10-15%- Data Sharing
  • 1. Reader accounts
  • 2. Direct sharing vs Data Marketplace
  • 3. Secure share creation and management
- Data Lakes
  • 1. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
  • 2. External tables and Iceberg support
  • 3. Snowflake as a Data Lake
- External Integrations
  • 1. External functions and UDFs
  • 2. Snowflake Connector for Python/Java
  • 3. Partner integrations (Spark, Kafka, etc.)

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q340-Q345):

NEW QUESTION # 340
A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters. Which solution will meet these requirements?

Answer: D

Explanation:
Athena query result reuse allows repeated queries with the same text and parameters to return previously computed results instead of scanning the source data again. This improves performance for repeated queries and reduces query costs by avoiding unnecessary data scans.


NEW QUESTION # 341
Select the correct usage statements with regards to SQL UDF?

Answer: B


NEW QUESTION # 342
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%. Which component of an AWS Glue Data Quality rule will meet these requirements?

Answer: D

Explanation:
AWS Glue Data Quality uses the ColumnValues rule with pattern matching to validate that values conform to a required format, such as an email pattern. Setting the threshold to greater than 0.9 ensures that at least 90% of the values in the Email column match the expected email format, causing the rule to fail if data quality drops below this level.


NEW QUESTION # 343
A company uses Amazon EMR as an extract, transform, and load (ETL) pipeline to transform data that comes from multiple sources. A data engineer must orchestrate the pipeline to maximize performance.
Which AWS service will meet this requirement MOST cost effectively?

Answer: A

Explanation:
Glue Workflows is for Glue job orchestration. C is for orchestration with different AWS services.


NEW QUESTION # 344
A data engineer is building a solution to detect sensitive information that is stored in a data lake across multiple Amazon S3 buckets. The solution must detect personally identifiable information (PII) that is in a proprietary data format.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
The AWS Glue Detect PII transform is a built-in feature that can automatically identify personally identifiable information (PII) using predefined patterns or custom regular expressions. It works directly within AWS Glue jobs on proprietary data formats and integrates with the Glue Data Catalog, delivering a managed, low-overhead solution compared to building and maintaining custom Lambda or SQL-based detection.


NEW QUESTION # 345
......

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