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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Micro-partitioning concepts 2. Table types (permanent, transient, temporary) 3. Hybrid and Hyper tables 4. Clustering strategies and key selection - Performance Optimization 1. Result set caching 2. Materialized views and cached results 3. Query profiling and execution plans 4. Warehouse scaling policies
Data Governance and Security	15-20%	- Data Protection 1. External tokenization 2. Data re-identification risks 3. Secure data sharing across accounts 4. Data masking and tokenization - Compliance and Monitoring 1. Tag-based policies 2. Access history and audit trails 3. Data classification - Access Control 1. Role hierarchy and ownership 2. Row-level and column-level security 3. Role-based access control (RBAC) 4. GRANT and REVOKE operations
Data Transformation	20-25%	- SQL-Based Transformations 1. Window functions for advanced analytics 2. Merge, update, and delete operations 3. Data type conversions and casting 4. Complex JOIN operations and optimizations - Data Processing Patterns 1. ELT vs ETL approaches in Snowflake 2. Time Travel for historical data access 3. Zero Copy Cloning for data provisioning - Streams and Tasks 1. Serverless task warehouse configuration 2. Task graph design and dependencies 3. Scheduled and event-based task execution 4. Change Data Capture (CDC) with streams
Advanced Snowflake Features	10-15%	- Data Lakes 1. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 2. Snowflake as a Data Lake 3. External tables and Iceberg support - External Integrations 1. External functions and UDFs 2. Partner integrations (Spark, Kafka, etc.) 3. Snowflake Connector for Python/Java - Data Sharing 1. Reader accounts 2. Direct sharing vs Data Marketplace 3. Secure share creation and management
Performance Engineering	10-15%	- Query Optimization 1. Join elimination and simplification 2. Predicate pushdown optimization 3. Query rewrite and optimization techniques - Warehouse Management 1. Multi-cluster warehouse configuration 2. Auto-suspend and auto-resume settings 3. Resource monitors and cost control
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 (Q214-Q219):

NEW QUESTION # 214
A company uses an Amazon RDS for Oracle database to store data for an ecommerce application. The company needs a solution to make the data in all tables available for future analytics. The solution must process data from a table named customer_complaints in near real time by using a REST API.
Which solution will meet these requirements MOST cost-effectively?

A. Set up one AWS Database Migration Service (AWS DMS) task to stream the customer_complaints table data to Amazon Kinesis Data Streams. Set up a second AWS DMS task to send data to an Amazon S3 bucket. Configure an AWS Lambda function to process Kinesis data and to call the REST API.
B. Create a read replica of the Amazon RDS for Oracle database. Configure an AWS Lambda function to process data from the read replica customer_complaints table and to call the REST API.
C. Create a task in AWS Database Migration Service (AWS DMS) to replicate the Oracle database to Amazon Redshift. Set the Oracle database as the source. Configure an AWS Lambda function to use the Amazon Redshift Data API to query the data from the customer_complaints table and to call the REST API. Unload the data from Amazon Redshift to Amazon S3.
D. Use AWS Database Migration Service (AWS DMS) to replicate the Oracle database to an Amazon RDS for PostgreSQL database instance. Create an AWS Lambda function to process data from the PostgreSQL customer_complaints table and to call the REST API.

Answer: A

Explanation:
AWS DMS can stream change data from the Oracle customer_complaints table to Kinesis Data Streams for near real-time processing by a Lambda that calls the REST API, while a separate DMS task delivers all tables to Amazon S3 for future analytics. This achieves both streaming and batch needs with minimal infrastructure and cost.

NEW QUESTION # 215
A company stores objects in an Amazon S3 bucket. The company crawls the objects so that Amazon Athena can query the data.
A data engineer manually moved all objects from the partition with a path prefix of status=01 to the prefix status=02. The status=01 partition location is now empty. However, the status=01 partition location still appears in the AWS Glue Data Catalog metadata.
Which Athena command should the data engineer run to resolve the metadata discrepancy?

A. ALTER TABLE CHANGE COLUMN
B. MSCK REPAIR TABLE
C. ALTER TABLE SET TBLPROPERTIES
D. ALTER TABLE DROP PARTITION

Answer: D

Explanation:
When data is manually moved and a partition location becomes empty, the AWS Glue Data Catalog still retains the old partition metadata. To resolve this discrepancy, the partition must be explicitly removed from the table metadata. The appropriate command deletes the outdated partition entry so that Athena reflects the current data layout.

NEW QUESTION # 216
A company is using Amazon Redshift as a data warehousing service. It is running analytical queries on large amounts of data using ODBC connections. The operations team is observing that some queries are getting completed while others are getting hung. They are seeking your suggestions to resolve this issue.
What setting can be done?

A. Set a lower fetch size per query.
B. Increase the size of MTU (Maximum Transmission Unit) to jumbo size.
C. Reduce the size of MTU (Maximum Transmission Unit).
D. Set a higher fetch size per query.

Answer: C

Explanation:
Maximum Transmission Unit (MTU) determines the maximum size in packet bytes that can be transferred in a single Ethernet frame between two hosts.
Customers may face query hang issues when the MTU size in a network path between two IP hosts is mismatched. To resolve the query hang issue for Redshift ODBC connections, the MTU size can be reduced.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/queries-troubleshooting.html#queries-troubleshooting-query-hangs
https://docs.aws.amazon.com/redshift/latest/mgmt/connecting-drop-issues.html

NEW QUESTION # 217
When would a Data engineer use table with the flatten function instead of the lateral flatten combination?

A. WhenTABLE with FLATTENrequires no additional source m the from clause to refer to
B. When TABLE with FLATTENrequires another source in the from clause to refer to
C. When table withFLATTENis acting like a sub-query executed for each returned row
D. Whenthe LATERALFLATTENcombination requires no other source m the from clause to refer to

Answer: B

Explanation:
Explanation
The TABLE function with the FLATTEN function is used to flatten semi-structured data, such as JSON or XML, into a relational format. The TABLE function returns a table expression that can be used in the FROM clause of a query. The TABLE function with the FLATTEN function requires another source in the FROM clause to refer to, such as a table, view, or subquery that contains the semi-structured data. For example:
SELECT t.value:city::string AS city, f.value AS population FROM cities t, TABLE(FLATTEN(input => t.value:population)) f; In this example, the TABLE function with the FLATTEN function refers to the cities table in the FROM clause, which contains JSON data in a variant column named value. The FLATTEN function flattens the population array within each JSON object and returns a table expression with two columns: key and value.
The query then selects the city and population values from the table expression.

NEW QUESTION # 218
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?

A. Use the Amazon S3 Storage Lens dashboard to gather the information.
B. Use Amazon S3 Inventory configurations reports to gather the information.
C. Use AWS usage reports for Amazon S3 to gather the information.
D. Use AWS CLI to gather the information.

Answer: A

Explanation:
Amazon S3 Storage Lens provides a comprehensive view of S3 storage usage and activity. It includes metrics such as the number of incomplete multipart uploads and the number of non- current object versions, making it an ideal tool for gathering insights into storage inefficiencies like outdated versions and incomplete uploads. This solution requires minimal operational effort as it is built into S3 and provides a user-friendly dashboard with detailed insights.
Using the AWS CLI to manually list and check multipart uploads and object versions would require scripting and regular execution, leading to higher operational overhead.
S3 Inventory provides reports on the objects in your bucket, but it does not directly provide information on incomplete multipart uploads or outdated versions. You would need to process the data further to gather the specific insights.
AWS usage reports are more focused on billing and usage rather than detailed storage optimization metrics like incomplete multipart uploads and outdated object versions.

NEW QUESTION # 219
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