Exam DEA-C01 Overview & Reliable DEA-C01 Test 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:	115 minutes
Certificate Validity Period:	2 years
Exam Price:	$375 USD
Real Exam Qty:	65
Related Certifications:	SnowPro Advanced: Architect SnowPro Advanced: Data Analyst SnowPro Advanced: Administrator SnowPro Core SnowPro Advanced: Security Engineer SnowPro Advanced: Data Scientist
Exam Format:	Multiple select, Multiple choice
Available Languages:	Japanese, English
Passing Score:	750 / 1000
Recommended Training:	SnowPro Advanced: Data Engineer Exam Study Guide Snowflake University Training Courses
Exam Registration:	Snowflake Certification Portal Pearson VUE Registration
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:

Topic	Details
Topic 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.
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	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	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	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
Ira a Data Engineer with TESLA IT systems, looking out to Compare Traditional Partitioning vs Snowflake micro-partitions for one of the Snowflake Project implementations. Which one of the following is incorrect understanding of Ira about Micro Partitioning?

A. Snowflake stores metadata about all rows stored in a micro-partition, including number of distinct columns.
B. All DML operations (e.g. DELETE, UPDATE, MERGE) take advantage of the under-lying micro-partition metadata to facilitate and simplify table maintenance.
C. In Snowflake, as data is inserted/loaded into a table, clustering metadata is collected and recorded for each micro-partition created during the process.
D. All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage compared to traditional partitioning where specialized DDL required.
E. The micro-partition metadata maintained by Snowflake enables precise pruning of col-umns in micro-partitions at query run-time, including columns containing semi-structured data.

Answer: A

Explanation:
Explanation
What are Micro-partitions?
All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage. Each micro-partition contains between 50 MB and 500 MB of uncompressed data (note that the actual size in Snowflake is smaller because data is always stored compressed). Groups of rows in tables are mapped into individual micro-partitions, organized in a columnar fashion. This size and structure allow for extremely granular pruning of very large tables, which can be comprised of millions, or even hundreds of millions, of micro-partitions.
Snowflake stores metadata about all rows stored in a micro-partition, including:
The range of values for each of the columns in the micro-partition.
The number of distinct values.
Additional properties used for both optimization and efficient query processing.
It Never stores number of columns as part of Metadata.
Rest of the statements are correct.

NEW QUESTION # 70
Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:

A. For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
B. For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
C. For Step 7, Results do include table changes committed by Transaction 1.
D. For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.
E. if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.

Answer: B

Explanation:
Explanation
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise, it stays at the same position.
Within Transaction 1, all queries to stream s1 see the same set of records. DML changes to table t1 are recorded to the stream only when the transaction is committed.
In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1. Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.

NEW QUESTION # 71
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

A. Bytes written - bytes written (e.g. when loading into a table).
B. Bytes read from result - bytes read from the result object.
C. External bytes scanned - bytes read from an external object, e.g. a stage.
D. Percentage scanned from cache - the percentage of data scanned from the local disk cache.
E. Bytes sent over the wireframe - amount of data sent over the wireframe

Answer: E

Explanation:
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details

NEW QUESTION # 72
A company is setting up a new Amazon SageMaker Unified Studio domain. Each of the company's business units needs isolated control over its own assets, projects, and metadata.
Specific datasets must be shareable with other business units upon approval. The company also requires centralized user authentication and identity mapping. Which solution will meet these requirements?

A. Configure each business unit as a domain unit with delegated ownership and fine-grained permissions policies. Give users the ability to share assets across domain units with explicit access control. Assign API keys to users for authentication to access the domain portal.
B. Configure business units to be represented as separate domains. Establish isolated environments with no shared administrative policies. Configure AWS IAM Identity Center for centralized authentication. Delegate administration at the domain level.
C. Configure each business unit as a separate domain unit to manage permissions on assets, projects, and metadata. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units. Enable cross-business unit sharing through access requests. Instruct domain unit owners to approve or deny the requests.
D. Configure business units as separate domain units with owner permissions. Restrict projects exclusively to owners to prevent data sharing between domains. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units.

Answer: C

Explanation:
SageMaker Unified Studio supports domain units within a single domain to provide isolation of assets, projects, and metadata while still allowing controlled sharing between units. Using AWS IAM Identity Center provides centralized authentication and identity mapping. Cross-unit sharing with approval workflows ensures datasets can be shared securely while maintaining isolation and least privilege.

NEW QUESTION # 73
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?

A. Python Connector
B. Node.js connector
C. Spark Connector
D. Kafka Connector

Answer: D

Explanation:
Explanation
Apache Kafka software uses a publish and subscribe model to write and read streams of records, similar to a message queue or enterprise messaging system. Kafka allows processes to read and write messages asynchronously. A subscriber does not need to be connected directly to a publisher; a pub-lisher can queue a message in Kafka for the subscriber to receive later.
An application publishes messages to a topic, and an application subscribes to a topic to receive those messages. Kafka can process, as well as transmit, messages; however, that is outside the scope of this document. Topics can be divided into partitions to increase scalability.
Kafka Connect is a framework for connecting Kafka with external systems, including databases. A Kafka Connect cluster is a separate cluster from the Kafka cluster. The Kafka Connect cluster sup-ports running and scaling out connectors (components that support reading and/or writing between external systems).
The Kafka connector is designed to run in a Kafka Connect cluster to read data from Kafka topics and write the data into Snowflake tables.
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns:
RECORD_CONTENT. This contains the Kafka message.
RECORD_METADATA. This contains metadata about the message, for example, the topic from which the message was read.

NEW QUESTION # 74
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