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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. Auto-suspend and auto-resume settings
  • 2. Resource monitors and cost control
  • 3. Multi-cluster warehouse configuration
Data Transformation20-25%- Streams and Tasks
  • 1. Task graph design and dependencies
  • 2. Scheduled and event-based task execution
  • 3. Serverless task warehouse configuration
  • 4. Change Data Capture (CDC) with streams
- 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. Window functions for advanced analytics
  • 2. Complex JOIN operations and optimizations
  • 3. Merge, update, and delete operations
  • 4. Data type conversions and casting
Data Governance and Security15-20%- Compliance and Monitoring
  • 1. Access history and audit trails
  • 2. Tag-based policies
  • 3. Data classification
- Data Protection
  • 1. Data masking and tokenization
  • 2. Secure data sharing across accounts
  • 3. External tokenization
  • 4. Data re-identification risks
- Access Control
  • 1. Row-level and column-level security
  • 2. Role-based access control (RBAC)
  • 3. GRANT and REVOKE operations
  • 4. Role hierarchy and ownership
Data Storage and Retrieval15-20%- Snowflake Architecture
  • 1. Hybrid and Hyper tables
  • 2. Clustering strategies and key selection
  • 3. Micro-partitioning concepts
  • 4. Table types (permanent, transient, temporary)
- Performance Optimization
  • 1. Materialized views and cached results
  • 2. Query profiling and execution plans
  • 3. Warehouse scaling policies
  • 4. Result set caching
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. Snowpipe configuration and usage
  • 2. Real-time data ingestion patterns
  • 3. Serverless compute for Snowpipe
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. Reader accounts
  • 2. Direct sharing vs Data Marketplace
  • 3. Secure share creation and management
- External Integrations
  • 1. Partner integrations (Spark, Kafka, etc.)
  • 2. External functions and UDFs
  • 3. Snowflake Connector for Python/Java

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?

Answer: A

Explanation:
Defining and creating the calculated field in the dataset ensures that the exchange rate calculations are precomputed before being stored in SPICE (QuickSight's super-fast, parallel, in- memory calculation engine). This allows the calculation to be materialized and reused across different analyses and visuals, ensuring that the precision is maintained and the performance is optimized.
Creating the calculated field in the analysis would compute the result at the analysis level, not within the dataset. This approach doesn't guarantee precomputation within SPICE and may increase the calculation overhead for each analysis.
Defining the field in the visual means the calculation is performed dynamically every time the visual is rendered, which does not leverage SPICE's precomputation benefits and can result in slower performance.
Creating the calculated field at the dashboard level is similar to doing it at the visual level, meaning the calculations are not precomputed and stored in SPICE, leading to potential performance issues and less reuse of the calculation across different analyses or visuals.


NEW QUESTION # 135
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys.
Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

Answer: D

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html


NEW QUESTION # 136
At what isolation level are Snowflake streams?

Answer: A

Explanation:
Explanation
The isolation level of Snowflake streams is repeatable read, which means that each transaction sees a consistent snapshot of data that does not change during its execution. Streams use time travel internally to provide this isolation level and ensure that queries on streams return consistent results regardless of concurrent transactions on their source tables.


NEW QUESTION # 137
A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
Which solution will meet this requirement?

Answer: D

Explanation:
Data Quality Ruleset: Creating a ruleset with Data Quality Definition Language (DQDL) rules allows for defining and evaluating data quality on specific AWS Glue tables, enabling automated checks on data quality.
Scheduled Execution: Running the ruleset daily ensures that data quality scores are regularly updated.
AWS Glue Data Source in Amazon DataZone: Configuring Amazon DataZone with an AWS Glue data source enables seamless integration, allowing data quality scores from AWS Glue Data Quality to be published to the Amazon DataZone portal.


NEW QUESTION # 138
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?

Answer: C

Explanation:
Using a single AWS Glue ETL job to both transform the data and invoke Glue Data Quality checks lets you declaratively enforce recommended rules without spinning up separate tools. You can then write the cleansed data and, if desired, the quality metrics, directly into your Amazon RDS for MySQL instance. This serverless, end-to-end approach minimizes service sprawl and only incurs Glue and RDS costs, making it the most cost-effective with the least operational overhead.


NEW QUESTION # 139
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