100% Pass Quiz DEA-C01 - SnowPro Advanced: Data Engineer Certification Exam–Trustable Review Guide

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Related Certifications:	SnowPro Core Certification
Available Languages:	English
Exam Price:	$375 USD
Certificate Validity Period:	2 years
Exam Duration:	115 minutes
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Real Exam Qty:	Approximately 65 questions
Exam Format:	Multiple select, Multiple choice, Scenario-based questions
Recommended Training:	SnowPro Advanced Data Engineer Exam Guide Snowflake University Training
Exam Registration:	Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or authorized testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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 (Q138-Q143):

NEW QUESTION # 138
A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.
A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).
Which solution will meet these requirements?

A. Define and create the calculated field in the dataset.
B. Define and create the calculated field in the analysis.
C. Define and create the calculated field in the visual.
D. Define and create the calculated field in the dashboard.

Answer: A

Explanation:
Defining and creating the calculated field in the dataset ensures that the exchange rate calculations are precomputed before being stored in SPICE (QuickSight's super-fast, parallel, in- memory calculation engine). This allows the calculation to be materialized and reused across different analyses and visuals, ensuring that the precision is maintained and the performance is optimized.
Creating the calculated field in the analysis would compute the result at the analysis level, not within the dataset. This approach doesn't guarantee precomputation within SPICE and may increase the calculation overhead for each analysis.
Defining the field in the visual means the calculation is performed dynamically every time the visual is rendered, which does not leverage SPICE's precomputation benefits and can result in slower performance.
Creating the calculated field at the dashboard level is similar to doing it at the visual level, meaning the calculations are not precomputed and stored in SPICE, leading to potential performance issues and less reuse of the calculation across different analyses or visuals.

NEW QUESTION # 139
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data.
Use AWS Glue jobs to transform the data and store the data in the S3 bucket.
B. Build an Amazon EventBridge based pipeline that invokes AWS Lambda functions to perform each stage.
C. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
D. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Manages Workflows tor Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.

Answer: A

Explanation:
AWS Step Functions provides managed orchestration with per-step retries, error handling, and execution history. Orchestrating Lambda for ingestion and AWS Glue jobs for transformation to S3 delivers a serverless, low-maintenance pipeline with built-in visibility into each stage's success or failure.

NEW QUESTION # 140
Pascal, a Data Engineer, have requirement to retrieve the 10 most recent executions of a specified task (completed, still running, or scheduled in the future) scheduled within the last hour, which of the following is the correct SQL Code ?

A. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,query_id IS NOT NULL
5.task_name=>'MYTASK'));
B. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
C. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK'));
D. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 11,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);

Answer: C

Explanation:
Explanation
To retrieve only tasks that are completed or still running, filter the query using WHERE query_id IS NOT NULL.

NEW QUESTION # 141
Which are false statements about Star Schema?

A. Star schemas are denormalized.
B. Star schema is more flexible in terms of analytical needs compared to Data Vault Mod-elling.
C. The star schema is an important special case of the snowflake schema and is more effec-tive for handling simpler queries.
D. The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes re-lated to fact data.

Answer: B

NEW QUESTION # 142
A manufacturing company has many IoT devices in facilities around the world. The company uses Amazon Kinesis Data Streams to collect data from the devices. The data includes device ID, capture date, measurement type, measurement value, and facility ID. The company uses facility ID as the partition key.
The company's operations team recently observed many WriteThroughputExceeded exceptions.
The operations team found that some shards were heavily used but other shards were generally idle.
How should the company resolve the issues that the operations team observed?

A. Change the partition key from facility ID to a randomly generated key.
B. Increase the number of shards.
C. Archive the data on the producer's side.
D. Change the partition key from facility ID to capture date.

Answer: A

Explanation:
The best solution to resolve the issue of uneven shard usage and WriteThroughputExceeded exceptions is to balance the load more evenly across the shards. This can be effectively achieved by changing the partition key to something that ensures a more uniform distribution of data across the shards.

NEW QUESTION # 143
......

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