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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q248-Q253):

NEW QUESTION # 248
A media streaming company is moving its application to AWS. The company wants to avoid data quality issues in user engagement analytics. The company needs to be able to reprocess several days of historical data without duplicates and without interrupting current real-time processing.
Which solution will meet these requirements?

Answer: B

Explanation:
Extended retention in Amazon Kinesis Data Streams allows the company to keep several days of historical records available for replay instead of being limited to the default 24 hours. To avoid duplicates during both replay and live consumption, consumers should be idempotent because Kinesis processing can deliver duplicate records in retry scenarios. Using DynamoDB for checkpointing is the standard fit with Kinesis consumer processing, and using separate processing paths lets the company replay historical data without disrupting ongoing real-time processing.


NEW QUESTION # 249
A transportation company wants to track vehicle movements by capturing geolocation records.
The records are 10 bytes in size. The company receives up to 10.000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?

Answer: D


NEW QUESTION # 250
A data engineer needs to deploy a serverless data pipeline. In the pipeline, CSV files are uploaded to an Amazon S3 bucket, which invokes an AWS Lambda function. The Lambda function transforms the CSV files to JSON format and stores the results in a second S3 bucket.
The data engineer has created an AWS Serverless Application Model (AWS SAM) template that includes the Lambda function. The data engineer wants to use AWS SAM for the pipeline deployment.
Which solution will package and deploy this serverless data pipeline?

Answer: D

Explanation:
AWS SAM can define both the Amazon S3 bucket and the S3 event source that invokes the Lambda function directly in the SAM template. The standard deployment flow is to run sam build to prepare the application artifacts and then run sam deploy --guided to package and deploy the serverless resources through AWS CloudFormation. This provides a complete infrastructure-as- code deployment for the pipeline without requiring manual trigger configuration.


NEW QUESTION # 251
A company stores information about its subscribers in an Amazon S3 bucket. The company runs an analysis every time a subscriber ends their subscription. The company uses AWS Lambda functions to respond to events from the S3 bucket by performing analyses.
The Lambda functions clean data from the S3 bucket and initiate an AWS Glue workflow. The Lambda functions have 128 MB of memory and 512 MB of ephemeral storage. The Lambda functions have a timeout of 15 seconds.
All three functions successfully finish running. However, CPU usage is often near 100%, which causes slow performance. The company wants to improve the performance of the functions and reduce the total runtime of the pipeline.
Which solution will meet these requirements?

Answer: A

Explanation:
In AWS Lambda, increasing memory also proportionally increases the allocated CPU. By raising the Lambda functions' memory from 128 MB to 512 MB, the functions receive more CPU power, which reduces execution time and improves performance without changing logic or timeouts.


NEW QUESTION # 252
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account B to process the data from the data stream. The company creates a Lambda execution role in Account B that has permissions to access data from the data stream in Account A.
What additional step must the company take to meet this requirement?

Answer: B

Explanation:
To enable cross-account Lambda processing of Kinesis Data Streams, the stream in Account A must explicitly allow the Lambda execution role from Account B. This is done by adding a resource-based policy on the Kinesis data stream to grant kinesis:SubscribeToShard and related read permissions to the cross-account role. Without this resource-based policy, the Lambda in Account B cannot consume the data.


NEW QUESTION # 253
......

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