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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 50 questions
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Exam Format:	Multiple Select, Multiple Choice
Available Languages:	English
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)
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	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	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	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 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 Sample Questions (Q333-Q338):

NEW QUESTION # 333
Melissa, Senior Data Engineer, looking out to optimize query performance for one of the Critical Control Dashboard, she found that most of the searches by the users on the control dashboards are based on Equality search on all the underlying columns mostly. Which Best techniques she should consider here?

A. She can go for clustering on underlying tables which can speedup Equality searches.
B. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
(Correct)
C. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
D. A materialized view speeds both equality searches and range searches.

Answer: B

Explanation:
Explanation
Clustering a table can speed any of the following, as long as they are on the clustering key:
Range searches.
Equality searches.
However, a table can be clustered on only a single key (which can contain one or more columns or expressions).
The search optimization service speeds equality searches. However, this applies to all the columns of supported types in a table that has search optimization enabled. This is what required here& best fit for purpose.
A materialized view speeds both equality searches and range searches, as well as some sort opera-tions, but only for the subset of rows and columns included in the materialized view.

NEW QUESTION # 334
A company generates yearly financial statements for customers and stores the statements in an Amazon S3 bucket. Customers rarely access the documents after 1 week. The company must retain the statements for 7 years. The statements must remain readily accessible for customers.
Which solution will meet these requirements in the MOST cost-effective way?

A. Set the S3 bucket to use S3 Intelligent-Tiering when new objects are uploaded. Set objects to expire after 7 years.
B. Create an S3 Lifecycle rule to transition objects to S3 Glacier Instant Retrieval after 7 days.
Expire the objects after 7 years.
C. Set the S3 bucket to use S3 Glacier Instant Retrieval when new objects are uploaded. Create an AWS Lambda function that runs daily to delete any objects that are older than 7 years.
D. Create an S3 Lifecycle rule to transition objects to S3 Glacier Deep Archive after 7 days. Expire the objects after 7 years.

Answer: B

Explanation:
S3 Glacier Instant Retrieval is the most cost-effective option that still keeps the statements readily accessible because it is designed for archival data that is rarely accessed but must be retrieved immediately, with millisecond access. A lifecycle rule can keep the files in a standard S3 class for the first 7 days, then transition them to Glacier Instant Retrieval for the remainder of the 7-year retention period, and expire them automatically at the end. S3 Glacier Deep Archive is cheaper but does not provide immediate access, and S3 Intelligent-Tiering is less cost-effective here because the access pattern is already known.
Reference:
https://aws.amazon.com/ru/s3/storage-classes/glacier/instant-retrieval/

NEW QUESTION # 335
A company is building a new application that ingests CSV files into Amazon Redshift. The company has developed the frontend for the application.
The files are stored in an Amazon S3 bucket. Files are no larger than 5 MB.
A data engineer is developing the extract, transform, and load (ETL) pipeline for the CSV files.
The data engineer configured a Redshift cluster and an AWS Lambda function that copies the data out of the files into the Redshift cluster.
Which additional steps should the data engineer perform to meet these requirements?

A. Configure an Amazon EventBridge rule that matches S3 new object created events. Set an Amazon Simple Queue Service (Amazon SQS) queue as the target of the rule. Configure the Lambda function to process the queue.
B. Configure the bucket to send S3 event notifications to Amazon EventBridge. Configure an EventBridge rule that matches S3 new object created events. Set the Lambda function as the target.
C. Configure the $3 bucket to send S3 event notifications to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the Lambda function to process the queue.
D. Configure AWS Database Migration Service (AWS DMS) to stream new S3 objects to a data stream in Amazon Kinesis Data Streams. Set the Lambda function as the target of the data stream.

Answer: B

Explanation:
By sending S3 "Object Created" events to EventBridge and matching those events with a rule that invokes your Lambda function, you trigger your ETL whenever a new CSV lands in S3, without extra polling or queue management. This direct, event-driven pattern keeps operational overhead to a minimum.

NEW QUESTION # 336
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between
100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?

A. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
B. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command.
Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger.
C. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.
D. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.

Answer: B

Explanation:
Configuring your S3 bucket to emit "Object Created" events into EventBridge and using a rule to invoke a lightweight Lambda function lets you automatically run a Redshift COPY for each new CSV. This serverless, event-driven pattern requires no continuously running infrastructure and has minimal operational overhead.

NEW QUESTION # 337
A Data Engineer is investigating a query that is taking a long time to return The Query Profile shows the following:

What step should the Engineer take to increase the query performance?

A. Rewrite the query using Common Table Expressions (CTEs)
B. Add additional virtual warehouses.
C. Change the order of the joins and start with smaller tables first
D. increasethe size of the virtual warehouse.

Answer: D

Explanation:
Explanation
The step that the Engineer should take to increase the query performance is to increase the size of the virtual warehouse. The Query Profile shows that most of the time was spent on local disk IO, which indicates that the query was reading a lot of data from disk rather than from cache. This could be due to a large amount of data being scanned or a low cache hit ratio. Increasing the size of the virtual warehouse will increase the amount of memory and cache available for the query, which could reduce the disk IO time and improve the query performance. The other options are not likely to increase the query performance significantly. Option A, adding additional virtual warehouses, will not help unless they are used in a multi-cluster warehouse configuration or for concurrent queries. Option C, rewriting the query using Common Table Expressions (CTEs), will not affect the amount of data scanned or cached by the query. Option D, changing the order of the joins and starting with smaller tables first, will not reduce the disk IO time unless it also reduces the amount of data scanned or cached by the query.

NEW QUESTION # 338
......

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