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Snowflake DEA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Transformation20-25%- 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
- 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
Topic 2: Data Governance and Security15-20%- Compliance and Monitoring
  • 1. Tag-based policies
  • 2. Access history and audit trails
  • 3. Data classification
- Data Protection
  • 1. External tokenization
  • 2. Data re-identification risks
  • 3. Secure data sharing across accounts
  • 4. Data masking and tokenization
- Access Control
  • 1. Row-level and column-level security
  • 2. GRANT and REVOKE operations
  • 3. Role-based access control (RBAC)
  • 4. Role hierarchy and ownership
Topic 3: Data Ingestion15-20%- Continuous Data Ingestion
  • 1. Serverless compute for Snowpipe
  • 2. Snowpipe configuration and usage
  • 3. Real-time data ingestion patterns
- 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 4: Performance Engineering10-15%- Warehouse Management
  • 1. Resource monitors and cost control
  • 2. Multi-cluster warehouse configuration
  • 3. Auto-suspend and auto-resume settings
- Query Optimization
  • 1. Join elimination and simplification
  • 2. Query rewrite and optimization techniques
  • 3. Predicate pushdown optimization
Topic 5: Data Storage and Retrieval15-20%- Performance Optimization
  • 1. Result set caching
  • 2. Materialized views and cached results
  • 3. Query profiling and execution plans
  • 4. Warehouse scaling policies
- Snowflake Architecture
  • 1. Clustering strategies and key selection
  • 2. Table types (permanent, transient, temporary)
  • 3. Micro-partitioning concepts
  • 4. Hybrid and Hyper tables
Topic 6: Advanced Snowflake Features10-15%- External Integrations
  • 1. External functions and UDFs
  • 2. Snowflake Connector for Python/Java
  • 3. Partner integrations (Spark, Kafka, etc.)
- Data Sharing
  • 1. Direct sharing vs Data Marketplace
  • 2. Secure share creation and management
  • 3. Reader accounts
- Data Lakes
  • 1. External tables and Iceberg support
  • 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
  • 3. Snowflake as a Data Lake

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Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q133-Q138):

133. Frage
A company uses an Amazon Redshift cluster to manage data, including vendor sales data. The company wants to store a copy of the vendor data in an Amazon S3 bucket.
A data engineer sets up an AWS Glue job to upload the data to the S3 bucket data on a schedule. The data engineer set up a network connection to allow private traffic between Amazon Redshift and Amazon S3.
What is the next step required to meet this requirement?

Antwort: C

Begründung:
Amazon Redshift needs an IAM role that grants permission to write to the target Amazon S3 bucket. After the private network path is in place, associating that role with the Redshift cluster enables the export operation to store the vendor data in S3.


134. Frage
SYSTEM$CLUSTERING_INFORMATION functions returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function returns a JSON object containing average_overlaps name/value pairs. Does High average_overlaps indicates well organized Clustering?

Antwort: B

Begründung:
Explanation
Higher the avg_overlap indicates poorly organized clustering.


135. Frage
Database XYZ has the data_retention_time_in_days parameter set to 7 days and table xyz.public.ABC has the data_retention_time_in_daysset to 10 days.
A Developer accidentally dropped the database containing this single table 8 days ago and just discovered the mistake.
How can the table be recovered?

Antwort: B

Begründung:
Explanation
The table can be recovered by using the undrop database xyz; command. This command will restore the database that was dropped within the last 14 days, along with all its schemas and tables, including the customer table. The data_retention_time_in_days parameter does not affect this command, as it only applies to time travel queries that reference historical data versions of tables or databases. The other options are not valid ways to recover the table. Option B is incorrect because creating a table as select * from xyz.public.ABC at
{offset => -6060248} will not work, as this query will try to access a historical data version of the ABC table that does not exist anymore after dropping the database. Option C is incorrect because creating a table clone xyz.public.ABC at {offset => -360024*3} will not work, as this query will try to clone a historical data version of the ABC table that does not exist anymore after dropping the database. Option D is incorrect because creating a Snowflake Support case to restore the database and table from fail-safe will not work, as fail-safe is only available for disaster recovery scenarios and cannot be accessed by customers.


136. Frage
A company uses a data stream in Amazon Kinesis Data Streams to collect transactional data from multiple sources. The company uses an AWS Glue extract, transform, and load (ETL) pipeline to look for outliers in the data from the stream. When the workflow detects an outlier, it sends a notification to an Amazon Simple Notification Service (Amazon SNS) topic. The SNS topic initiates a second workflow to retrieve logs for the outliers and stores the logs in an Amazon S3 bucket.
The company experiences delays in the notifications to the SNS topic during periods when the data stream is processing a high volume of data. When the company examines Amazon CloudWatch logs, the company notices a high value for the glue.driver.BlockManager.disk.diskSpaceUsed_MB metric when the traffic is high. The company must resolve this issue.
Which solution will meet this requirement with the LEAST operational effort?

Antwort: B

Begründung:
The high BlockManager disk usage indicates memory pressure and spilling due to under- provisioned Spark executors. Enabling AWS Glue auto scaling lets the job automatically add workers during traffic spikes, alleviating memory/disk pressure and reducing delays with minimal operational effort.


137. Frage
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?

Antwort: A,C

Begründung:
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.


138. Frage
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