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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
If external software i.e. TIBCO, exports Data fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, How Snowflake handle it? [Select 2]

Answer: C

Explanation:
Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.


NEW QUESTION # 22
Which UDF programming language is not supported with Snowflake Secure Data Sharing feature?

Answer: C


NEW QUESTION # 23
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics. Which solution will meet these requirements with the LEAST ongoing maintenance?

Answer: D

Explanation:
Amazon Kinesis Data Streams is well suited to collect high-volume real-time clickstream data from websites and mobile apps, and Amazon Data Firehose can then deliver that streaming data to Amazon S3 with very little operational effort. This approach is highly managed and avoids the ongoing administration required for EC2-based collectors, self-managed Kafka patterns, or maintaining a provisioned Redshift cluster for raw landing storage.
Reference:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/firehose/latest/dev/basic-deliver.html


NEW QUESTION # 24
Which output is provided by both theSYSTEM$CLUSTERING_DEPTHfunction and theSYSTEM$CLUSTERING_INFORMATIONfunction?

Answer: A

Explanation:
Explanation
The output that is provided by both the SYSTEM$CLUSTERING_DEPTH function and the SYSTEM$CLUSTERING_INFORMATION function is average_depth. This output indicates the average number of micro-partitions that contain data for a given column value or combinationof column values. The other outputs are not common to both functions. The notes output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it contains additional information or recommendations about the clustering status of the table. The average_overlaps output is only provided by the SYSTEM$CLUSTERING_DEPTH function and it indicates the average number of micro-partitions that overlap with other micro-partitions for a given column value or combination of column values. The total_partition_count output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it indicates the total number of micro-partitions in the table.


NEW QUESTION # 25
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.

Answer: B,C,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 # 26
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