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| Section | Weight | Objectives |
|---|
| Topic 1: Data Ingestion | 15-20% | - Continuous Data Ingestion
- 1. Real-time data ingestion patterns
- 2. Snowpipe configuration and usage
- 3. Serverless compute for Snowpipe
- Bulk Data Loading
- 1. Data loading error handling and validation
- 2. Staging configurations (internal and external stages)
- 3. File format considerations (CSV, JSON, Parquet, Avro)
- 4. COPY INTO command options and best practices
|
| Topic 2: Advanced Snowflake Features | 10-15% | - Data Sharing
- 1. Direct sharing vs Data Marketplace
- 2. Reader accounts
- 3. Secure share creation and management
- External Integrations
- 1. Partner integrations (Spark, Kafka, etc.)
- 2. Snowflake Connector for Python/Java
- 3. External functions and UDFs
- Data Lakes
- 1. Snowflake as a Data Lake
- 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
- 3. External tables and Iceberg support
|
| Topic 3: Data Governance and Security | 15-20% | - Data Protection
- 1. Secure data sharing across accounts
- 2. Data masking and tokenization
- 3. External tokenization
- 4. Data re-identification risks
- 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
- Compliance and Monitoring
- 1. Tag-based policies
- 2. Access history and audit trails
- 3. Data classification
|
| Topic 4: Data Storage and Retrieval | 15-20% | - Performance Optimization
- 1. Materialized views and cached results
- 2. Warehouse scaling policies
- 3. Query profiling and execution plans
- 4. Result set caching
- Snowflake Architecture
- 1. Clustering strategies and key selection
- 2. Hybrid and Hyper tables
- 3. Table types (permanent, transient, temporary)
- 4. Micro-partitioning concepts
|
| Topic 5: Performance Engineering | 10-15% | - Warehouse Management
- 1. Resource monitors and cost control
- 2. Auto-suspend and auto-resume settings
- 3. Multi-cluster warehouse configuration
- Query Optimization
- 1. Join elimination and simplification
- 2. Query rewrite and optimization techniques
- 3. Predicate pushdown optimization
|
| Topic 6: Data Transformation | 20-25% | - Data Processing Patterns
- 1. ELT vs ETL approaches in Snowflake
- 2. Time Travel for historical data access
- 3. Zero Copy Cloning for data provisioning
- SQL-Based Transformations
- 1. Window functions for advanced analytics
- 2. Data type conversions and casting
- 3. Complex JOIN operations and optimizations
- 4. Merge, update, and delete operations
- Streams and Tasks
- 1. Change Data Capture (CDC) with streams
- 2. Serverless task warehouse configuration
- 3. Scheduled and event-based task execution
- 4. Task graph design and dependencies
|
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q98-Q103):
NEW QUESTION # 98
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?
- A. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
- B. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
- C. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
- D. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
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 # 99
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?
- A. AWS Glue Studio
- B. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
- C. AWS Step Functions
- D. AWS Glue workflows
Answer: D
Explanation:
https://aws.amazon.com/blogs/big-data/orchestrate-an-etl-pipeline-using-aws-glue-workflows- triggers-and-crawlers-with-custom-classifiers/
https://aws.amazon.com/blogs/big-data/extracting-multidimensional-data-from-microsoft-sql- server-analysis-services-using-aws-glue/
NEW QUESTION # 100
A data engineer needs to analyze time-sensitive sales data. The company stores the data in an Amazon S3 bucket. The data engineer uses AWS Glue Data Catalog to access the data.
When the data engineer performs the analysis, the data engineer notices that some records are missing or out of date.
What is the likely cause of these issues?
- A. Versioning is not enabled on the S3 bucket.
- B. The AWS Glue job schedules overlap with one another.
- C. AWS Glue Data Catalog is not up to date with the latest S3 partition changes.
- D. Incorrect IAM roles are assigned to the AWS Glue jobs.
Answer: C
Explanation:
Missing or outdated records typically occur when the AWS Glue Data Catalog has not been updated to reflect the latest partitions in the Amazon S3 bucket. The catalog must be crawled or updated so queries and analyses can see the most recent data.
NEW QUESTION # 101
An insurance company stores transaction data that the company compressed with gzip.
The company needs to query the transaction data for occasional audits.
Which solution will meet this requirement in the MOST cost-effective way?
- A. Store the data in Amazon S3. Use Amazon S3 Select to query the data.
- B. Store the data in Amazon S3. Use Amazon Athena to query the data.
- C. Store the data in Amazon Glacier Instant Retrieval. Use Amazon Athena to query the data.
- D. Store the data in Amazon Glacier Flexible Retrieval. Use Amazon S3 Glacier Select to query the data.
Answer: D
NEW QUESTION # 102
A company maintains a central Amazon Redshift data warehouse that aggregates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer notices that some complex transformation queries take hours to finish. The data engineer wants to optimize query performance to reduce query execution time as much as possible.
Which solution will meet this requirement?
- A. Use materialized views in Amazon Redshift for frequently queried data patterns.
- B. Increase the concurrency scaling quota for the Redshift cluster.
- C. Use Amazon Redshift Spectrum to create external tables based on the Redshift tables.
- D. Export the tables to an Amazon S3 bucket. Use Amazon Athena to query the data in the bucket.
Answer: A
Explanation:
Amazon Redshift materialized views precompute and store results of complex queries, allowing subsequent queries to run much faster by reading from the precomputed data. This significantly reduces execution time for frequently used transformation patterns.
NEW QUESTION # 103
......
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