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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation	20-25%	- Streams and Tasks 1. Change Data Capture (CDC) with streams 2. Serverless task warehouse configuration 3. Task graph design and dependencies 4. Scheduled and event-based task execution - 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. Data type conversions and casting 2. Window functions for advanced analytics 3. Complex JOIN operations and optimizations 4. Merge, update, and delete operations
Performance Engineering	10-15%	- Query Optimization 1. Predicate pushdown optimization 2. Join elimination and simplification 3. Query rewrite and optimization techniques - Warehouse Management 1. Auto-suspend and auto-resume settings 2. Resource monitors and cost control 3. Multi-cluster warehouse configuration
Advanced Snowflake Features	10-15%	- 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. Partner integrations (Spark, Kafka, etc.) 2. Snowflake Connector for Python/Java 3. External functions and UDFs - Data Sharing 1. Direct sharing vs Data Marketplace 2. Reader accounts 3. Secure share creation and management
Data Governance and Security	15-20%	- Compliance and Monitoring 1. Access history and audit trails 2. Tag-based policies 3. Data classification - Data Protection 1. Secure data sharing across accounts 2. Data masking and tokenization 3. Data re-identification risks 4. External tokenization - Access Control 1. Row-level and column-level security 2. Role hierarchy and ownership 3. GRANT and REVOKE operations 4. Role-based access control (RBAC)
Data Ingestion	15-20%	- Bulk Data Loading 1. COPY INTO command options and best practices 2. File format considerations (CSV, JSON, Parquet, Avro) 3. Staging configurations (internal and external stages) 4. Data loading error handling and validation - Continuous Data Ingestion 1. Snowpipe configuration and usage 2. Real-time data ingestion patterns 3. Serverless compute for Snowpipe
Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Hybrid and Hyper tables 2. Clustering strategies and key selection 3. Micro-partitioning concepts 4. Table types (permanent, transient, temporary) - Performance Optimization 1. Materialized views and cached results 2. Warehouse scaling policies 3. Query profiling and execution plans 4. Result set caching

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q242-Q247):

NEW QUESTION # 242
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format.
The company is running a new Oracle database as a source system in the company's data center. The company has 70 tables in the Oracle database. All the tables have primary keys.
Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake.
Which solution will meet this requirement with the LEAST effort?

A. Create an Oracle database in Amazon RDS. Use AWS Database Migration Service (AWS DMS) to migrate the on-premises Oracle database to Amazon RDS. Configure triggers on the tables to invoke AWS Lambda functions to write changed records to Amazon S3 in Parquet format.
B. Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.
C. Create an AWS Glue connection to the Oracle database. Create an AWS Glue bookmark job to ingest the data incrementally and to write the data to Amazon S3 in Parquet format.
D. Create an Apache Sqoop job in Amazon EMR to read the data from the Oracle database.
Configure the Sqoop job to write the data to Amazon S3 in Parquet format.

Answer: B

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html

NEW QUESTION # 243
Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:

A. For Step 7, Results do include table changes committed by Transaction 1.
B. For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.
C. For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
D. For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
E. if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.

Answer: C

Explanation:
Explanation
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise, it stays at the same position.
Within Transaction 1, all queries to stream s1 see the same set of records. DML changes to table t1 are recorded to the stream only when the transaction is committed.
In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1. Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.

NEW QUESTION # 244
If the data retention period for a table is less than 90 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale?

A. FALSE
B. TRUE

Answer: A

Explanation:
Explanation
If the data retention period for a table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to prevent it from going stale. The period is extended to the stream's offset, up to a maximum of 14 days by default, regardless of the Snowflake edition for your account. The maximum number of days for which Snowflake can extend the data retention period is determined by the MAX_DATA_EXTENSION_TIME_IN_DAYS parameter value. When the stream is consumed, the extended data retention period is reduced to the default period for the table.

NEW QUESTION # 245
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

A. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
B. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
C. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
D. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

Answer: D

Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html

NEW QUESTION # 246
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?

A. YES
B. NO

Answer: B

Explanation:
Explanation
Higher the avg_overlap indicates poorly organized clustering.

NEW QUESTION # 247
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