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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Exam Price:$375 USD
Passing Score:750 / 1000
Related Certifications:SnowPro Advanced: Data Scientist
SnowPro Advanced: Administrator
SnowPro Advanced: Security Engineer
SnowPro Advanced: Data Analyst
SnowPro Core
SnowPro Advanced: Architect
Certificate Validity Period:2 years
Available Languages:English, Japanese
Real Exam Qty:65
Exam Duration:115 minutes
Exam Format:Multiple select, Multiple choice
Recommended Training:SnowPro Advanced: Data Engineer Exam Study Guide
Snowflake University Training Courses
Exam Registration:Pearson VUE Registration
Snowflake Certification Portal
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Must hold active SnowPro Core Certification; recommended 2+ years hands-on data engineering experience
Official Syllabus URL:https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 2
  • Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 3
  • Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 4
  • Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 5
  • Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
A company needs to send customer call data from its on-premises PostgreSQL database to AWS to generate near real-time insights. The solution must capture and load updates from operational data stores that run in the PostgreSQL database. The data changes continuously.
A data engineer configures an AWS Database Migration Service (AWS DMS) ongoing replication task. The task reads changes in near real time from the PostgreSQL source database transaction logs for each table. The task then sends the data to an Amazon Redshift cluster for processing.
The data engineer discovers latency issues during the change data capture (CDC) of the task.
The data engineer thinks that the PostgreSQL source database is causing the high latency.
Which solution will confirm that the PostgreSQL database is the source of the high latency?

Answer: D

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Troubleshooting_Latency.html A high CDCLatencySource metric indicates that the process of capturing changes from the source is delayed.


NEW QUESTION # 92
An insurance company stores transaction data that the company compressed with gzip.
The company needs to query the transaction data for occasional audits.
Which solution will meet this requirement in the MOST cost-effective way?

Answer: C


NEW QUESTION # 93
An ecommerce company uses AWS Glue ETL to process and analyze orders. The company wants to build an extract, transform, and load (ETL) pipeline that processes placed, shipped, delivered, and canceled orders differently.
The company integrates the order processing system with Amazon EventBridge. The company configures EventBridge Scheduler rules for each order status to invoke different AWS Glue workflows. When the company examines Amazon CloudWatch metrics for the workflow, the company notices that the FailedInvocations metric shows a high value for canceled orders.
The company must determine the cause of the failed invocations.
Which solution will meet this requirement?

Answer: C

Explanation:
Configuring a dead-letter queue for EventBridge Scheduler captures the events that could not be delivered to the target along with the failure context. Reviewing the failed canceled-order events in the DLQ provides the most direct way to identify why the invocations are failing (for example, payload issues, permission errors, or target configuration problems) without changing retry behavior.


NEW QUESTION # 94
A Data Engineer would like to define a file structure for loading and unloading data Where can the file structure be defined? (Select THREE)

Answer: A,C,D

Explanation:
Explanation
The places where the file format can be defined are copy command, file format object, and stage object. These places allow specifying or referencing a file format that defines how data files are parsed and loaded into or unloaded from Snowflake tables. A file format can include various options, such as field delimiter, field enclosure, compression type, date format, etc. The other options are not places where the file format can be defined. Option B is incorrect because MERGE command is a SQL command that can merge data from one table into another based on a join condition, but it does not involve loading or unloading data files. Option D is incorrect because pipe object is a Snowflake object that can load data from an external stage into a Snowflake table using COPY statements, but it does not define or reference a file format. Option F is incorrect because INSERT command is a SQL command that can insert data into a Snowflake table from literal values or subqueries, but it does not involve loading or unloading data files.


NEW QUESTION # 95
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?

Answer: B

Explanation:
Since the ETL job reaches maximum CPU usage but not memory usage, switching from general- purpose instances to compute-optimized instances (such as C5 or C6g instances) can provide better performance per dollar for CPU-bound workloads.


NEW QUESTION # 96
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