DEA-C01 SnowPro Advanced: Data Engineer Certification Exam Pass4sure Zertifizierung & SnowPro Advanced: Data Engineer Certification Exam zuverlässige Prüfung Übung

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	Approximately 65 questions
Exam Duration:	115 minutes
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Exam Format:	Multiple select, Scenario-based questions, Multiple choice
Related Certifications:	SnowPro Core Certification
Exam Price:	$375 USD
Certificate Validity Period:	2 years
Available Languages:	English
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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DEA-C01 Torrent Anleitung - DEA-C01 Studienführer & DEA-C01 wirkliche Prüfung

Die Snowflake DEA-C01 Zertifizierungsprüfung ist sehr populär in IT-Industrie. Es spielt eine übergreifende Bedeutung für die Leute, die ihre Arbeitsstelle erhöhen wollen. Und es ist auch die Wahl, die Leute klar sehen können. Außerdem dadurch können Sie Ihre Fähigkeit verbessern und mehr verwendbare Technik beherrschen. Damit können Sie Ihre Arbeit besser fertigen und auch anderen Ihre Fähigkeit zeigen.

Snowflake DEA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 3	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.
Thema 4	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.
Thema 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q193-Q198):

193. Frage
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3.
Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?

A. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size.
For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
B. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
C. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
D. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.

Antwort: A

Begründung:
Changing the Firehose output format to Apache Parquet reduces Amazon Athena query costs because Parquet is a columnar storage format, which is much more efficient for queries compared to row-based formats like JSON. Parquet helps reduce the amount of data scanned by Athena, thus lowering costs.
Configuring Firehose to use a large buffer size ensures fewer, larger files, which also improves query performance in Athena.
For the existing JSON data, using an AWS Glue ETL job to convert the files into Parquet and apply the necessary partitioning (e.g., YYYYMMDD) will help optimize future queries without needing to recreate the data pipeline. Using the ALTER TABLE ADD PARTITION statement in Athena will allow the existing table to reflect the new partitions.
This solution minimizes management effort by reconfiguring Amazon Kinesis Data Firehose and using AWS Glue for handling existing data, without requiring significant changes to the existing pipeline.
Running an Apache Spark job on Amazon EMR requires more operational management, as you would need to launch and manage ephemeral EMR clusters daily, which increases complexity.
Amazon Managed Service for Apache Flink introduces additional components (Kinesis Data Streams and Flink), which adds complexity and management overhead compared to using the existing Firehose and simply changing its output format.
Using Lambda for real-time transformation and combining that with a Glue ETL job introduces unnecessary complexity. Firehose already supports direct output to Parquet format, making the Lambda function redundant.

194. Frage
A data engineer needs to run a data transformation job whenever a user adds a file to an Amazon S3 bucket. The job will run for less than 1 minute. The job must send the output through an email message to the data engineer. The data engineer expects users to add one file every hour of the day.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Create an AWS Lambda function to transform the data. Use Amazon S3 Event Notifications to invoke the Lambda function when a new object is created. Publish the output to an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe the data engineer's email account to the topic.
B. Create a small Amazon EC2 instance that polls the S3 bucket for new files. Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.
C. Deploy an Amazon EMR cluster. Use EMR File System (EMRFS) to access the files in the S3 bucket. Run transformation code on a schedule to generate the output to a second S3 bucket.Create an Amazon Simple Notification Service (Amazon SNS) topic. Configure Amazon S3 Event Notifications to notify the topic when a new object is created.
D. Run an Amazon Elastic Container Service (Amazon ECS) task to poll the S3 bucket for new files.
Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.

Antwort: A

Begründung:
An S3 event notification on ObjectCreated can directly invoke a Lambda function as soon as a new file arrives. Lambda can perform your <1 minute transformation and then publish the result to an SNS topic. With the data engineer subscribed to that topic by email, the output is delivered automatically
- no servers to manage, no polling, and minimal ongoing operations.

195. Frage
Search optimization works best to improve the performance of a query when the following condi-tions are true:[Select All that apply]

A. The table is not clustered.
B. The table is frequently queried on columns other than the primary cluster key.
C. Search Query uses Equality predicates (for example, <column_name> = <constant>) OR Predicates that use IN.
D. Search Query uses Sort Operations.

Antwort: A,B,C

Begründung:
Explanation
Materialized Views works best for search query performance in case of Sort Operations. For Rest of the points Search optimization works best to improve query performance.

196. Frage
A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?

A. Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
B. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
C. Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.
D. Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

Antwort: A

Begründung:
Two step approach involving creating a staging table, followed by using Redshift's merge statement to update the target table from staging table and finally truncate/housekeep the staging table.

197. Frage
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
B. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
C. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
D. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

Antwort: A

Begründung:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html

198. Frage
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