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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q235-Q240):

NEW QUESTION # 235
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?

Answer: C

Explanation:
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.


NEW QUESTION # 236
A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?

Answer: B


NEW QUESTION # 237
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

Answer: C

Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.


NEW QUESTION # 238
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)

Answer: B,C

Explanation:
Choosing KEY distribution on both tables with a high-cardinality join column colocates matching rows across nodes and avoids data skew, improving join performance.
Redshift Advisor provides automated, actionable recommendations (e.g., distribution and sort keys, stats) to further optimize the slow query with minimal effort.


NEW QUESTION # 239
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?

Answer: B

Explanation:
DynamoDB Streams can capture changes to items in DynamoDB tables (such as inserts, updates, and deletes) in near real-time. An AWS Lambda function can be triggered by these streams, and the function can process the changes and update the data in Amazon OpenSearch Service. This solution meets the requirement for near real-time updates with minimal latency.
The "Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service." solution involves exporting data periodically, which does not meet the requirement for near real- time updates. Periodic exports could introduce delays.
AWS Glue is generally used for batch processing, and it is not designed for near real-time data ingestion. It would not be an ideal solution for real-time or low-latency requirements.
The "Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables." option suggests building a custom solution, which would involve significant development effort.
Moreover, OpenSearch does not natively support such plugins, making this solution less practical compared to using DynamoDB Streams and Lambda, which are designed to work together.


NEW QUESTION # 240
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