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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion	15-20%	- Bulk Data Loading 1. Data loading error handling and validation 2. File format considerations (CSV, JSON, Parquet, Avro) 3. COPY INTO command options and best practices 4. Staging configurations (internal and external stages) - Continuous Data Ingestion 1. Real-time data ingestion patterns 2. Serverless compute for Snowpipe 3. Snowpipe configuration and usage
Topic 2: Performance Engineering	10-15%	- Query Optimization 1. Predicate pushdown optimization 2. Query rewrite and optimization techniques 3. Join elimination and simplification - Warehouse Management 1. Resource monitors and cost control 2. Auto-suspend and auto-resume settings 3. Multi-cluster warehouse configuration
Topic 3: Advanced Snowflake Features	10-15%	- External Integrations 1. Partner integrations (Spark, Kafka, etc.) 2. External functions and UDFs 3. Snowflake Connector for Python/Java - Data Sharing 1. Direct sharing vs Data Marketplace 2. Reader accounts 3. Secure share creation and management - Data Lakes 1. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 2. Snowflake as a Data Lake 3. External tables and Iceberg support
Topic 4: Data Storage and Retrieval	15-20%	- Performance Optimization 1. Query profiling and execution plans 2. Warehouse scaling policies 3. Result set caching 4. Materialized views and cached results - Snowflake Architecture 1. Table types (permanent, transient, temporary) 2. Micro-partitioning concepts 3. Hybrid and Hyper tables 4. Clustering strategies and key selection
Topic 5: Data Governance and Security	15-20%	- Compliance and Monitoring 1. Access history and audit trails 2. Data classification 3. Tag-based policies - 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 Protection 1. External tokenization 2. Data masking and tokenization 3. Secure data sharing across accounts 4. Data re-identification risks
Topic 6: Data Transformation	20-25%	- Streams and Tasks 1. Task graph design and dependencies 2. Change Data Capture (CDC) with streams 3. Scheduled and event-based task execution 4. Serverless task warehouse configuration - SQL-Based Transformations 1. Merge, update, and delete operations 2. Data type conversions and casting 3. Complex JOIN operations and optimizations 4. Window functions for advanced analytics - Data Processing Patterns 1. Zero Copy Cloning for data provisioning 2. Time Travel for historical data access 3. ELT vs ETL approaches in Snowflake

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q306-Q311):

NEW QUESTION # 306
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

A. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
B. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

Answer: C

NEW QUESTION # 307
An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.
B. Use an S3 bucket that is in the same account that uses Athena to query the data.
C. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.
D. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.
E. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.

Answer: A,D

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html

NEW QUESTION # 308
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.
B. Grant the AWS Glue job IAM role access to the stored credentials.
C. Store the credentials in the AWS Glue job parameters.
D. Store the credentials in a configuration file that is in an Amazon S3 bucket.
E. Store the credentials in AWS Secrets Manager.

Answer: B,E

NEW QUESTION # 309
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics. Which solution will meet these requirements with the LEAST ongoing maintenance?

A. Deploy agents on Amazon EC2 instances to collect the streaming data. Use the AWS CLI to periodically batch upload the data to an Amazon S3 bucket.
B. Use Amazon Data Firehose to ingest the streaming data. Deliver the processed data directly to a provisioned Amazon Redshift cluster.
C. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) broker to collect the data.Store the data in an Amazon RDS DB instance.
D. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.

Answer: D

Explanation:
Amazon Kinesis Data Streams is well suited to collect high-volume real-time clickstream data from websites and mobile apps, and Amazon Data Firehose can then deliver that streaming data to Amazon S3 with very little operational effort. This approach is highly managed and avoids the ongoing administration required for EC2-based collectors, self-managed Kafka patterns, or maintaining a provisioned Redshift cluster for raw landing storage.
Reference:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/firehose/latest/dev/basic-deliver.html

NEW QUESTION # 310
A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?

A. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.
B. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
C. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.
D. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances.
Update the network connections to interact with the newly deployed EC2 instances.

Answer: C

Explanation:
Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.

NEW QUESTION # 311
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