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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
Snowflake web interface can be used to create users with no passwords or remove passwords from existing users?

A. TRUE
B. FALSE

Answer: B

Explanation:
Explanation
You cannot use the Snowflake web interface to create users with no passwords or remove pass-words from existing users. You must use CREATE USER or ALTER USER command instead.
Also If you create a user with no password (or alter an existing user and remove their password), this effectively disables Snowflake authentication for the user. Without a password in Snowflake, a user cannot log in using Snowflake authentication and must use federated authentication instead.

NEW QUESTION # 140
A global ecommerce company occasionally receives customer data files in its Amazon S3 data lake. The company needs to automatically detect new data and mask sensitive data before making the data available to the company's analytics team.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure Amazon S3 Event Notifications to detect the new data and trigger an AWS Glue job.
Use Amazon Macie to detect and mask the sensitive data. Store the processed data in Amazon Redshift.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to detect incoming data. Use Amazon EMR workflows to detect and mask sensitive data. Store the processed data in Amazon S3.
C. Use Amazon EventBridge to detect new data and run AWS Glue workflows. Use AWS Glue DataBrew to detect and mask the sensitive data. Store the processed data in an S3 bucket.
D. Use Amazon Kinesis Data Streams to capture new data. Use Amazon Comprehend to detect and mask the sensitive data. Store the processed data in Amazon DynamoDB tables.

Answer: C

Explanation:
Amazon EventBridge can detect new object events in Amazon S3 and trigger an automated workflow. AWS Glue workflows provide a managed way to orchestrate the processing, and AWS Glue DataBrew can detect and mask sensitive data before writing the cleansed output back to Amazon S3. This meets the requirement with the least operational overhead because it uses managed services for both event detection and data masking, whereas Amazon Macie is primarily for discovering and reporting sensitive data in S3 rather than masking it in-place as part of the preparation pipeline.

NEW QUESTION # 141
What is a characteristic of the operations of streams in Snowflake?

A. Each committed and uncommitted transaction on the source table automatically puts a change record in the stream.
B. When a stream is used to update a target table the offset is advanced to the current time.
C. Whenever a stream is queried, the offset is automatically advanced.
D. Querying a stream returns all change records and table rows from the current offset to the current time.

Answer: D

Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream has an offset, which is a point in time that marks the beginning of the change records to be returned by the stream. Querying a stream returns all change records and table rows from the current offset to the current time. The offset is not automatically advanced by querying the stream, but it can be manually advanced by using the ALTER STREAM command. When a stream is used to update a target table, the offset is advanced to the current time only if the ON UPDATE clause is specified in the stream definition. Each committed transaction on the source table automatically puts a change record in the stream, but uncommitted transactions do not.

NEW QUESTION # 142
A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?

A. Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
B. figure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket.
C. Configure an Amazon EventBridge rule to react when the execution status of a state machine changes. Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.
D. Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run.

Answer: C

Explanation:
Amazon EventBridge can monitor the execution status of AWS Step Functions state machines and trigger specific actions based on state changes, such as when a state machine succeeds or fails. By configuring an EventBridge rule to capture these execution status changes and forward the details to an Amazon SNS topic, real-time notifications can be sent to alert users. This solution ensures a scalable and event-driven approach to monitoring the entire data processing pipeline in real time.

NEW QUESTION # 143
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
B. Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values.
Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
C. Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.
D. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.

Answer: D

Explanation:
AWS Glue workflows and data quality actions allow for seamless integration with AWS Glue to ensure that data quality checks are automatically performed after ETL jobs. This solution leverages the existing AWS Glue infrastructure and EventBridge to trigger notifications without needing complex orchestration tools like Apache Airflow or EMR, resulting in lower operational overhead.
Amazon EventBridge can easily be configured to trigger a notification to Amazon SNS when a dataset is incomplete, providing a highly automated and low-maintenance solution.
Airflow requires managing a separate cluster and configuring Directed Acyclic Graphs (DAGs), which introduces more operational complexity and overhead than AWS Glue workflows.
Using Amazon EMR introduces unnecessary complexity and higher operational overhead, as it requires provisioning and managing a cluster for Spark jobs, which is more complex than using AWS Glue and EventBridge.
While Lambda can be a lightweight option, orchestrating Lambda functions for data quality checks with Step Functions adds unnecessary complexity when AWS Glue workflows already offer built-in capabilities for data quality actions with lower overhead.

NEW QUESTION # 144
......

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