Quiz Snowflake - Authoritative DEA-C01 - Valid Test SnowPro Advanced: Data Engineer Certification Exam Experience

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Snowflake DEA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Transformation20-25%- Streams and Tasks
  • 1. Scheduled and event-based task execution
  • 2. Change Data Capture (CDC) with streams
  • 3. Task graph design and dependencies
  • 4. Serverless task warehouse configuration
- SQL-Based Transformations
  • 1. Complex JOIN operations and optimizations
  • 2. Data type conversions and casting
  • 3. Merge, update, and delete operations
  • 4. Window functions for advanced analytics
- Data Processing Patterns
  • 1. Time Travel for historical data access
  • 2. Zero Copy Cloning for data provisioning
  • 3. ELT vs ETL approaches in Snowflake
Topic 2: Data Governance and Security15-20%- Data Protection
  • 1. Data re-identification risks
  • 2. External tokenization
  • 3. Secure data sharing across accounts
  • 4. Data masking and tokenization
- Access Control
  • 1. Row-level and column-level security
  • 2. GRANT and REVOKE operations
  • 3. Role-based access control (RBAC)
  • 4. Role hierarchy and ownership
- Compliance and Monitoring
  • 1. Access history and audit trails
  • 2. Data classification
  • 3. Tag-based policies
Topic 3: Performance Engineering10-15%- Warehouse Management
  • 1. Resource monitors and cost control
  • 2. Multi-cluster warehouse configuration
  • 3. Auto-suspend and auto-resume settings
- Query Optimization
  • 1. Predicate pushdown optimization
  • 2. Join elimination and simplification
  • 3. Query rewrite and optimization techniques
Topic 4: Data Ingestion15-20%- 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. Data loading error handling and validation
  • 4. Staging configurations (internal and external stages)
Topic 5: Data Storage and Retrieval15-20%- Performance Optimization
  • 1. Query profiling and execution plans
  • 2. Result set caching
  • 3. Materialized views and cached results
  • 4. Warehouse scaling policies
- Snowflake Architecture
  • 1. Hybrid and Hyper tables
  • 2. Clustering strategies and key selection
  • 3. Table types (permanent, transient, temporary)
  • 4. Micro-partitioning concepts
Topic 6: Advanced Snowflake Features10-15%- Data Lakes
  • 1. External tables and Iceberg support
  • 2. Snowflake as a Data Lake
  • 3. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
- Data Sharing
  • 1. Direct sharing vs Data Marketplace
  • 2. Reader accounts
  • 3. Secure share creation and management
- 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 (Q341-Q346):

NEW QUESTION # 341
UDTFs also called a table function, returns zero, one, or multiple rows for each input row?

Answer: B

Explanation:
Explanation
UDFs may be scalar or tabular.
A scalar function returns one output row for each input row. The returned row consists of a single column/value.
A tabular function, also called a table function, returns zero, one, or multiple rows for each input row. A tabular UDF is defined by specifying a return clause that contains the TABLE keyword and specifies the names and data types of the columns in the table results. Tabular UDFs are often called UDTFs (user-defined table functions) or table UDFs.


NEW QUESTION # 342
Tasks may optionally use table streams to provide a convenient way to continuously process new or changed data. A task can transform new or changed rows that a stream surfaces. Each time a task is scheduled to run, it can verify whether a stream contains change data for a table and either consume the change data or skip the current run if no change data exists. Which System Function can be used by Data engineer to verify whether a stream contains changed data for a table?

Answer: D

Explanation:
Explanation
SYSTEM$STREAM_HAS_DATA
Indicates whether a specified stream contains change data capture (CDC) records.


NEW QUESTION # 343
A data engineer is building a data orchestration workflow. The data engineer plans to use a hybrid model that includes some on-premises resources and some resources that are in the cloud. The data engineer wants to prioritize portability and open source resources.
Which service should the data engineer use in both the on-premises environment and the cloud- based environment?

Answer: C

Explanation:
Amazon MWAA is a managed service for Apache Airflow, which is an open-source workflow automation tool. Apache Airflow can be used both on-premises and in the cloud, making it ideal for hybrid environments. Using Amazon MWAA allows the data engineer to leverage the managed service in the cloud while maintaining the ability to use the same open-source Airflow setup on-premises, ensuring portability and consistency across environments.


NEW QUESTION # 344
A company runs a data platform on AWS. The data platform uses AWS Glue to provide a data catalog and to perform processing. The company notices quality issues in the data.
The company needs to implement data quality validations. The validations must include rules for known issues. The validations must have the ability to automatically detect unexpected data quality issues.
Which solution will meet these requirements with the LEAST operation overhead?

Answer: B

Explanation:
AWS Glue Data Quality provides native, managed data quality rules with built-in anomaly detection, allowing validation of known issues while automatically identifying unexpected data quality problems with minimal operational effort.


NEW QUESTION # 345
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
AWS Glue DataBrew is a fully managed, serverless service with built-in data quality rulesets and profiling jobs that run directly on S3 data. Triggering the profile job via EventBridge on object creation applies rules with minimal code and management overhead.


NEW QUESTION # 346
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