DEA-C01 Hot Spot Questions - DEA-C01 Valid Study Notes

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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 Duration:	90 minutes
Certificate Validity Period:	2 years (recertification required)
Exam Price:	$375 USD
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Exam Format:	Multiple Choice, Multiple Select
Real Exam Qty:	approximately 50 questions
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Available Languages:	English
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:	SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:	https://www.snowflake.com/certification/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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 3	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.
Topic 4	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 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
A company needs to store semi-structured transactional data for an application in a database.
The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds. Which solution will meet these requirements with the LEAST operational overhead?

A. Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.
B. Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.
C. Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.
D. Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.

Answer: B

Explanation:
Amazon DynamoDB is a fully managed serverless database that is designed for millisecond response times and works well for semi-structured application data. Because the application reads frequently and writes infrequently, adding DynamoDB Accelerator (DAX) provides an in- memory cache that further reduces read latency with minimal operational overhead.

NEW QUESTION # 117
A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)

A. Upgrade to a larger instance size.
B. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
C. Reboot the RDS DB instance once each week.
D. Modify the database schema to include additional tables and indexes.
E. Implement caching to reduce the database query load.

Answer: A,B

NEW QUESTION # 118
A company runs an Apache Spark application every night in an Amazon EMR cluster. The company uses Amazon EC2 instances to supply compute capacity for the EMR cluster. The company deployed the Spark application in cluster mode.
An error occurs in the Spark application. A log for the error is stored in the application's Spark driver standard error logs. A data engineer needs to investigate the error.
Where can the data engineer find this error log?

A. The engineer can connect to the persistent application UI to see the first YARN container log in the Spark UI.
B. The engineer can connect to the Amazon EMR console to see the Amazon EMR step logs that are archived in Amazon S3.
C. The engineer can connect to the web UI on the live cluster to see the YARN ResourceManager logs.
D. The engineer can connect to the primary node of the cluster by using SSH to see the Spark history server logs.

Answer: B

Explanation:
In EMR cluster mode, the Spark driver runs inside the cluster and its standard error logs are captured as step logs. These logs are automatically archived to Amazon S3 and accessible from the Amazon EMR console under step logs. This is the correct location for investigating Spark driver errors.

NEW QUESTION # 119
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
B. Use Amazon Aurora PostgreSQL to load all the data into Aurora.
C. Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).
D. Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).

Answer: C

Explanation:
Amazon Redshift Serverless is ideal for this scenario as it provides a fully managed, serverless data warehouse solution with high performance for analytics queries. Key reasons why it meets the requirements with the least operational overhead:
Serverless Configuration: Redshift Serverless automatically scales capacity based on query demand, making it well-suited for unpredictable spikes in usage without requiring manual adjustments.
Managed Storage (RMS): Loading data into Amazon Redshift managed storage provides optimized storage for analytics and integrates with Amazon QuickSight for fast query performance in direct query mode.
Cost-Effective and Low Management: Since the data warehouse is only actively used for a few hours daily, Redshift Serverless provides a cost- effective option by scaling resources dynamically, which eliminates the need to manage and pay for idle infrastructure.

NEW QUESTION # 120
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks.
The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student's selected language. Which solution will meet these requirements with the LEAST operational overhead?

A. Create a knowledge base in Amazon Bedrock Knowledge Bases with the university's textbooks.
Configure a multilingual model to generate responses with source citations.
B. Use Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.
C. Use Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university's textbooks to generate personalized feedback with citations.
D. Build a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queues the database when generating responses with Amazon Bedrock.

Answer: A

Explanation:
Amazon Bedrock Knowledge Bases is the lowest-overhead solution because it provides a managed retrieval-augmented generation workflow over the university's textbooks and can return generated responses with citations to the source material. The RetrieveAndGenerate capability is designed to generate answers from retrieved textbook content and include source citations, which directly matches the requirement for accurate references to specific course materials. By choosing a multilingual model in Bedrock for generation, the application can respond in the student's selected language without building and operating a custom retrieval stack.
Reference:
https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html
https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-retrieve-generate.html
https://docs.aws.amazon.com/bedrock/latest/userguide/kb-how-retrieval.html

NEW QUESTION # 121
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