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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Semi-Structured and Advanced Data Handling	- Advanced transformation techniques 1. Flattening nested data 2. Complex transformations in SQL - Semi-structured data processing 1. VARIANT data type usage 2. JSON, Avro, Parquet handling
Topic 2: Security, Governance, and Data Sharing	- Data governance 1. Data masking policies 2. Secure data sharing concepts - Access control 1. Object-level permissions 2. Role-based access control (RBAC)
Topic 3: Performance Optimization	- Warehouse optimization 1. Scaling strategies 2. Concurrency management - Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms
Topic 4: Data Loading and Integration	- Bulk data loading 1. File format handling 2. COPY INTO command usage - Snowpipe and continuous ingestion 1. Automated ingestion workflows 2. Event-driven loading
Topic 5: Data Pipelines and Transformation	- Streams and Tasks 1. Automating pipelines with Tasks 2. Change Data Capture (CDC) with Streams - ETL/ELT design patterns in Snowflake 1. Data orchestration concepts 2. Staging and transformation layers
Topic 6: Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Storage and compute separation 2. Virtual warehouses and compute scaling - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account.
A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow.
Which log type should the data engineer use to diagnose the cause of the failure?

A. YourEnvironmentName-WebServer
B. YourEnvironmentName-Scheduler
C. YourEnvironmentName-DAGProcessing
D. YourEnvironmentName-Task

Answer: D

Explanation:
When a workflow fails to run in Amazon MWAA, the task logs (YourEnvironmentName-Task) are the most relevant for diagnosing the issue. Task logs contain detailed information about the execution of individual tasks within the workflow, including any error messages or stack traces that can help pinpoint the cause of the failure.

NEW QUESTION # 175
Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]

A. Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.
B. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.
C. Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
D. Snowflake supports resizing a warehouse at any time, even while running.
E. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.

Answer: A,B,C,D,E

Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
Snowflake supports resizing a warehouse at any time, even while running. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following:
Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing.
Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.

NEW QUESTION # 176
A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer.
A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Certificate Manager (ACM).
B. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.
C. Implement custom automation scripts in AWS Secrets Manager.
D. Store self-managed certificates on the EC2 instances.

Answer: A

Explanation:
AWS Certificate Manager (ACM) simplifies the process of generating, distributing, and managing SSL/TLS certificates. ACM allows you to automatically renew and deploy certificates with minimal operational overhead. Once integrated with services like EC2, ACM can handle the entire lifecycle of the certificates, from creation to renewal and deployment, without manual intervention.
Managing certificates manually introduces significant operational overhead. You would need to handle generation, deployment, renewal, and rotation manually, which is error-prone and time- consuming.
While you could store certificates in AWS Secrets Manager, you would still need to implement custom scripts for certificate generation, rotation, and deployment, increasing operational complexity compared to ACM.
Amazon ECS Service Connect is used for simplifying service discovery and communication within ECS. While it can secure traffic between ECS services, it is not designed for managing SSL/TLS certificates on EC2 instances.

NEW QUESTION # 177
A retail company stores data from a product lifecycle management (PLM) application in an on- premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on- premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

A. Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.
B. Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.
C. Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.
D. Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.

Answer: B

Explanation:
https://aws.amazon.com/ko/blogs/apn/change-data-capture-from-on-premises-sql-server-to- amazon-redshift-target/

NEW QUESTION # 178
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

A. AWS Glue
B. Amazon Redshift
C. Amazon EMR
D. AWS Lambda

Answer: C

Explanation:
Glue is like the more good-looking one, but weaker brother of EMR. So when it's about petabyte scales, let EMR do the work and have Glue stay away from the action.

NEW QUESTION # 179
......

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