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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Performance Engineering	10-15%	- Query Optimization 1. Query rewrite and optimization techniques 2. Predicate pushdown optimization 3. Join elimination and simplification - Warehouse Management 1. Auto-suspend and auto-resume settings 2. Multi-cluster warehouse configuration 3. Resource monitors and cost control
Topic 2: Data Transformation	20-25%	- 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 Processing Patterns 1. ELT vs ETL approaches in Snowflake 2. Zero Copy Cloning for data provisioning 3. Time Travel for historical data access - 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
Topic 3: Data Governance and Security	15-20%	- Access Control 1. GRANT and REVOKE operations 2. Role-based access control (RBAC) 3. Role hierarchy and ownership 4. Row-level and column-level security - Data Protection 1. Data re-identification risks 2. Secure data sharing across accounts 3. External tokenization 4. Data masking and tokenization - Compliance and Monitoring 1. Tag-based policies 2. Data classification 3. Access history and audit trails
Topic 4: Advanced Snowflake Features	10-15%	- External Integrations 1. External functions and UDFs 2. Partner integrations (Spark, Kafka, etc.) 3. Snowflake Connector for Python/Java - 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. Secure share creation and management 2. Reader accounts 3. Direct sharing vs Data Marketplace
Topic 5: Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Micro-partitioning concepts 2. Hybrid and Hyper tables 3. Clustering strategies and key selection 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
Topic 6: Data Ingestion	15-20%	- Continuous Data Ingestion 1. Real-time data ingestion patterns 2. Serverless compute for Snowpipe 3. Snowpipe configuration and usage - Bulk Data Loading 1. Staging configurations (internal and external stages) 2. Data loading error handling and validation 3. File format considerations (CSV, JSON, Parquet, Avro) 4. COPY INTO command options and best practices

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q159-Q164):

NEW QUESTION # 159
Which ones are the false statements about Materialized Views?

A. A materialized view can also be used as the data source for a subquery.
B. Snowflake does not allow standard DML (e.g. INSERT, UPDATE, DELETE) on ma-terialized views.
C. Materialized views can be secure views.
D. Clustering a subset of the materialized views on a table tends to be more cost-effective than clustering the table itself.
E. Snowflake does not allow users to truncate materialized views.
F. Materialized views are first-class account objects.

Answer: F

Explanation:
Explanation
Materialized views are first-class Database objects & rest of the understandings are true.

NEW QUESTION # 160
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation. Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to
15%.
B. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
C. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
D. Deploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.

Answer: A

Explanation:
AWS Step Functions Distributed Map is designed to process large datasets from Amazon S3 in parallel with minimal infrastructure management. The ToleratedFailurePercentage setting directly supports the requirement to stop the overall workflow when more than 15% of records fail validation, which makes it the lowest-overhead solution for this use case.

NEW QUESTION # 161
Pascal, a Data Engineer, have requirement to retrieve the 10 most recent executions of a specified task (completed, still running, or scheduled in the future) scheduled within the last hour, which of the following is the correct SQL Code ?

A. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 11,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
B. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
C. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,query_id IS NOT NULL
5.task_name=>'MYTASK'));
D. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK'));

Answer: D

Explanation:
Explanation
To retrieve only tasks that are completed or still running, filter the query using WHERE query_id IS NOT NULL.

NEW QUESTION # 162
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

A. Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.
B. Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis
C. Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.
D. Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.

Answer: C

Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.

NEW QUESTION # 163
A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files.
The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline.
The company needs to improve the performance of the second pipeline.
Which solution will meet this requirement MOST cost-effectively?

A. Increase the number of workers in the AWS Glue ETL jobs.
B. Use the AWS Glue DynamicFrame grouping option.
C. Enable AWS Glue auto scaling.
D. Use a larger worker type.

Answer: B

Explanation:
Using the AWS Glue DynamicFrame grouping option (for example, groupFiles=True with an appropriate groupSize) combines many small input files into larger partitions at read time. This reduces the per-file overhead of task initialization and metadata operations, yielding much faster ETL runs without the added cost of more or bigger workers.

NEW QUESTION # 164
......

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