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| Section | Weight | Objectives |
|---|
| Topic 1: Data Architecture and Processing | 20% | - Data Modeling for Performance
- 1. Dimension handling
- 2. Slowly changing dimensions (SCD)
- 3. Star and snowflake schemas
- Data Pipeline Design
- 1. Stream and task patterns
- 2. Pipeline monitoring and error handling
- 3. Data scheduling and orchestration
- Data Storage Architecture
- 1. Micro-partitioning and clustering
- 2. Table types (Permanent, Transient, Temporary)
- 3. Hybrid Tables concepts
|
| Topic 2: Data Ingestion and Consumption | 20% | - Continuous Data Loading
- 1. Real-time data ingestion patterns
- 2. Automating data loading with tasks
- 3. Snowpipe configuration and usage
- Data Unloading
- 1. Unloading to internal and external stages
- 2. Data export best practices
- 3. Partitioning unloading data
- Bulk Loading and Unloading
- 1. Data loading performance optimization
- 2. Handling staged files
- 3. File format options (CSV, JSON, Parquet, AVRO)
- 4. COPY INTO command options and best practices
|
| Topic 3: Security and Governance | 15% | - Governance and Compliance
- 1. Row access policies
- 2. Access history and auditing
- 3. Object tagging
- 4. Data retention policies
- Access Control
- 1. GRANT and REVOKE operations
- 2. Role-based access control (RBAC)
- 3. Role hierarchy and ownership
- Data Security
- 1. Column-level security
- 2. External tokenization
- 3. Data masking and tokenization
- 4. Row-level security policies
|
| Topic 4: Data Transformation with Snowflake | 30% | - Snowflake Scripting
- 1. Error handling
- 2. Procedures and control flow
- 3. Dynamic SQL
- SQL Transformations
- 1. Working with semi-structured data (VARIANT)
- 2. Window functions advanced usage
- 3. Data type conversions and handling
- 4. Complex JOINs and set operations
- Data Processing Patterns
- 1. Zero-copy cloning for ETL
- 2. MERGE, UPDATE, DELETE operations
- 3. Time travel and change data capture
|
| Topic 5: Performance Optimization | 15% | - Warehouse Performance
- 1. Multi-cluster warehouses
- 2. Warehouse sizing and selection
- 3. Resource monitors
- 4. Warehouse scaling policies
- Data Optimization
- 1. Materialized views
- 2. Search optimization service
- 3. Data cache management
- Query Optimization
- 1. Indexing strategies with clustering
- 2. Avoiding common performance pitfalls
- 3. Query profiling and analysis
- 4. Query result caching
|
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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q260-Q265):
NEW QUESTION # 260
You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?
- A. Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
- B. Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
- C. Set 'snowflake.ingest.file.name' to an existing file in a stage.
- D. Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
- E. Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
Answer: D
Explanation:
The correct answer is E. Currently, the Snowflake Kafka connector does not directly support automatic schema evolution. You cannot configure the connector to automatically alter the Snowflake table schema based on changes in the Kafka topic's data structure. You must manually manage schema changes in the Snowflake table to align with the structure of the data being ingested from Kafka. Option D will simply throw errors as the configuration needed is not fully complete with data types. The connector does rely heavily on the VARIANT column and would not be able to evolve properly, and so, that function is not directly available.
NEW QUESTION # 261
You are tasked with building a data pipeline that incrementally loads data from an external cloud storage location (AWS S3) into a Snowflake table named 'SALES DATA'. You want to optimize the pipeline for cost and performance. Which combination of Snowflake features and configurations would be MOST efficient and cost-effective for this scenario, assuming the data volume is substantial and constantly growing?
- A. Use a Snowflake Task to regularly truncate and reload 'SALES DATA" from S3 using COPY INTO. This ensures data consistency.
- B. Use a Snowflake Task scheduled every 5 minutes to execute a COPY INTO command from S3, with no file format specified, assuming the data is CSV and auto-detection will work.
- C. Create an external stage pointing to the S3 bucket. Create a Snowpipe with auto-ingest enabled, using an AWS SNS topic and SQS queue for event notifications. Configure the pipe with an error notification integration to monitor ingestion failures.
- D. Employ a third-party ETL tool to extract data from S3, transform it, and load it into Snowflake using JDBC. Schedule the ETL process using the tool's built-in scheduler.
- E. Develop a custom Python script that uses the Snowflake Connector for Python to connect to Snowflake and execute a COPY INTO command. Schedule the script to run on an EC2 instance using cron.
Answer: C
Explanation:
Snowpipe with auto-ingest is the most efficient and cost-effective solution for continuously loading data into Snowflake from cloud storage. It leverages event notifications to trigger data loading as soon as new files are available, minimizing latency and compute costs. Option A lacks error handling and proper file format specification. Option C involves custom coding and infrastructure management. Option D introduces overhead and costs associated with a third-party ETL tool. Option E is inefficient as it truncates and reloads the entire table, losing any incremental loading benefits.
NEW QUESTION # 262
A data engineer observes that a Snowflake query, used for generating a daily sales report, consistently runs slower each day, despite the dataset size remaining relatively stable. The query joins a large sales table (SALES) with a smaller product dimension table (PRODUCT) on PRODUCT ID. You've already confirmed that virtual warehouse sizing is adequate and data clustering is enabled on SALES(SALE DATE). Analyze the following scenarios and identify the MOST likely cause of the performance degradation and potential solution:
- A. The Snowflake query optimizer is selecting a suboptimal execution plan because the query is complex. Rewrite the query using temporary tables to break down the logic into smaller steps.
- B. The statistics on the PRODUCT table are outdated. Run 'ANALYZE TABLE PRODUCT' to refresh them.
- C. The virtual warehouse is being overwhelmed by concurrent queries. Implement workload management rules to prioritize the sales report query.
- D. The join between SALES and PRODUCT is causing excessive data spill to local storage due to an inefficient join order. Consider using a JOIN hint, specifically a BROADCAST hint on the PRODUCT table (small table).
- E. The data clustering on SALES(SALE DATE) is ineffective as the query also filters on PRODUCT CATEGORY, which isn't clustered. Re-cluster SALES by both SALE DATE and PRODUCT CATEGORY.
Answer: D
Explanation:
The most likely cause is inefficient join processing leading to data spilling. Broadcasting the smaller PRODUCT table to all nodes improves join performance by avoiding data redistribution. Option A might help, but broadcasting is more impactful. Option C is incorrect because only one column can be explicitly used for clustering. Option D is a last resort; the optimizer should handle a simple join. Option E is less likely if the overall data volume hasn't changed.
NEW QUESTION # 263
You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?
- A. The data files in cloud storage are not being automatically detected by Snowpipe.
- B. The Snowpipe is paused due to exceeding the daily quota.
- C. The file format definition in Snowflake is incompatible with the Avro schema.
- D. Snowflake does not support Avro format for Snowpipe.
- E. The cloud storage event notifications are not properly configured to trigger Snowpipe.
Answer: E
Explanation:
Option D is the most likely reason. Snowpipe relies on cloud storage event notifications (e.g., SQS for AWS S3, Event Grid for Azure Blob Storage, Pub/Sub for Google Cloud Storage) to trigger data ingestion. If these notifications are not properly configured, Snowpipe will not be notified of new files being added to the storage location, resulting in skipped files. Option A is possible, but less likely if the pipe was just created and initial data loading is failing. Option B is incorrect; Snowpipe detects files based on event notifications, not by continuously scanning the storage location. Option C could be an issue, but the question states the file format is defined. Option E is incorrect; Snowpipe does support Avro format.
NEW QUESTION # 264
A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?
- A. Increase the parameter for the 'SALES_DATX table.
- B. Implement data clustering on a column that is frequently used in time travel query filters.
- C. Periodically clone the ' SALES DATA' table to a separate historical table.
- D. Decrease the parameter for the 'SALES_DATA' table.
- E. Create a materialized view that pre-computes the results of common time travel queries.
Answer: B
Explanation:
Clustering (E) optimizes the physical storage of data, making it more efficient to retrieve specific historical versions. Decreasing data retention (C) can improve performance but reduces the available time travel window. Increasing retention (A) will degrade performance even more. Materialized Views (B) can help, but they require maintenance and may not be suitable for all time travel scenarios. Cloning (D) creates a full copy, which is inefficient and may not be necessary.
NEW QUESTION # 265
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