DEA-C01 PDF Questions - Lab DEA-C01 Questions

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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Predicate pushdown optimization 2. Query rewrite and optimization techniques 3. Join elimination and simplification
Topic 2: Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Table types (permanent, transient, temporary) 2. Clustering strategies and key selection 3. Hybrid and Hyper tables 4. Micro-partitioning concepts - Performance Optimization 1. Query profiling and execution plans 2. Warehouse scaling policies 3. Materialized views and cached results 4. Result set caching
Topic 3: 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. File format considerations (CSV, JSON, Parquet, Avro) 3. Staging configurations (internal and external stages) 4. COPY INTO command options and best practices
Topic 4: Advanced Snowflake Features	10-15%	- Data Sharing 1. Direct sharing vs Data Marketplace 2. Reader accounts 3. Secure share creation and management - Data Lakes 1. External tables and Iceberg support 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 3. Snowflake as a Data Lake - External Integrations 1. Snowflake Connector for Python/Java 2. External functions and UDFs 3. Partner integrations (Spark, Kafka, etc.)
Topic 5: Data Transformation	20-25%	- Streams and Tasks 1. Serverless task warehouse configuration 2. Change Data Capture (CDC) with streams 3. Scheduled and event-based task execution 4. Task graph design and dependencies - Data Processing Patterns 1. Time Travel for historical data access 2. ELT vs ETL approaches in Snowflake 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 6: Data Governance and Security	15-20%	- Data Protection 1. Data re-identification risks 2. Secure data sharing across accounts 3. External tokenization 4. Data masking and tokenization - Access Control 1. Role-based access control (RBAC) 2. Role hierarchy and ownership 3. GRANT and REVOKE operations 4. Row-level and column-level security - Compliance and Monitoring 1. Data classification 2. Access history and audit trails 3. Tag-based policies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q338-Q343):

NEW QUESTION # 338
A company needs to optimize storage for an Amazon S3 bucket. Objects older than 1 year must be accessible within 5 hours. All versions of the objects must be retained and immutable for 7 years. All versions of the objects must use the write-once-read-many (WORM) model. Which solution will meet these requirements?

A. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
B. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to delete objects that are older than 7 years.
C. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
D. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to deletes objects that are older than 7 years.

Answer: C

Explanation:
S3 Object Lock enforces the write-once-read-many model and ensures that all object versions remain immutable for the required 7-year retention period. Transitioning objects older than 1 year to S3 Glacier Flexible Retrieval satisfies the requirement that data be accessible within 5 hours, as this storage class supports retrieval within minutes to a few hours. The lifecycle policy can expire objects after 7 years, aligning with the retention requirement once the Object Lock retention period has ended.

NEW QUESTION # 339
A CSV file around 1 TB in size is generated daily on an on-premise server A corresponding table. Internal stage, and file format have already been created in Snowflake to facilitate the data loading process How can the process of bringing the CSV file into Snowflake be automated using the LEAST amount of operational overhead?

A. Create a task in Snowflake that executes once a day and runs a copy into statement that references the internal stage The internal stage will read the files directly from the on-premise server and copy the newest file into the table from the on-premise server to the Snowflake table
B. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage. Create a pipe that runs a copy into statement that references the internal stage Snowpipe auto-ingest will automatically load the file from the internal stage when the new file lands in the internal stage.
C. On the on-premise server schedule a SQL file to run using SnowSQL that executes a PUT to push a specific file to the internal stage Create a task that executes once a day m Snowflake and runs a OOPY WTO statement that references the internal stage Schedule the task to start after the file lands in the internal stage
D. On the on premise server schedule a Python file that uses the Snowpark Python library. The Python script will read the CSV data into a DataFrame and generate an insert into statement that will directly load into the table The script will bypass the need to move a file into an internal stage

Answer: B

Explanation:
Explanation
This option is the best way to automate the process of bringing the CSV file into Snowflake with the least amount of operational overhead. SnowSQL is a command-line tool that can be used to execute SQL statements and scripts on Snowflake. By scheduling a SQL file that executes a PUT command, the CSV file can be pushed from the on-premise server to the internal stage in Snowflake. Then, by creating a pipe that runs a COPY INTO statement that references the internal stage, Snowpipe can automatically load the file from the internal stage into the table when it detects a new file in the stage. This way, there is no need to manually start or monitor a virtual warehouse or task.

NEW QUESTION # 340
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

A. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
B. Use the Amazon Redshift Data API.
C. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
D. Establish WebSocket connections to Amazon Redshift.

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html

NEW QUESTION # 341
PARTITION_TYPE = USER_SPECIFIED must be used when you prefer to add and remove par-titions selectively rather than automatically adding partitions for all new files in an external storage location that match an expression?

A. TRUE
B. FALSE

Answer: A

Explanation:
Explanation
The CREATE EXTERNAL TABLE syntax for manually added partitions is as follows:
1.CREATE EXTERNAL TABLE
2.<table_name>
3.( <part_col_name> <col_type> AS <part_expr> )
4.[ , ... ]
5.[ PARTITION BY ( <part_col_name> [, <part_col_name> ... ] ) ]
6.PARTITION_TYPE = USER_SPECIFIED
Included the required PARTITION_TYPE = USER_SPECIFIED parameter.

NEW QUESTION # 342
Select the correct usage statements with regards to SQL UDF?

A. All of above are correct.
B. Scalar functions (UDFs) have a limit of 500 input arguments.
C. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
D. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
E. You can include only one query expression.

Answer: A

NEW QUESTION # 343
......

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