DEA-C01 Free Exam, New DEA-C01 Exam Pattern

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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.

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DEA-C01 Test Dumps: SnowPro Advanced: Data Engineer Certification Exam - DEA-C01 Actual Exam Questions

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which connector creates the RECORD_CONTENT and RECORD_METADATA columns in the existing Snowflake table while connecting to Snowflake?

A. Node.js connector
B. Spark Connector
C. Python 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 # 66
Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).

A. HASH COMPARE(*)
B. HASH (*)
C. HASH_AGG(*)
D. HASH_AGG(<expr>, <expr>)
E. HASH_AGG_COMPARE (*)

Answer: C,D

Explanation:
Explanation
The functions that will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results are:
HASH_AGG(*): This function computes a hash value over all columns and rows in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
HASH_AGG(<expr>, <expr>): This function computes a hash value over two expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
HASH (*): This function computes a hash value over all columns in a single row. The function returns one value per row, not one value per table, query result, or window.
HASH_AGG_COMPARE (): This function compares two hash values computed by HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
HASH COMPARE(): This function compares two hash values computed by HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.

NEW QUESTION # 67
Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]

A. Resizing can help reduce the queuing that occurs if a warehouse does not have enough compute resources to process all the queries that are submitted concurrently.
B. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
C. Snowflake supports resizing a warehouse at any time, even while running.
D. Scaling up is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
E. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.

Answer: A,B,C,D,E

Explanation:
Explanation
Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account).
Snowflake supports resizing a warehouse at any time, even while running. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following:
Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing.
Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries.
Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.

NEW QUESTION # 68
Which are the two ways to access elements in a JSON object?

A. Use SemiColon notation to traverse a path in a JSON object:
<col-umn>:<level1_element>;<level2_element>;<level3_element>.
B. use bracket notation to traverse the path in an object:
<col-umn>['<level1_element>']['<level2_element>'].
C. use Curly bracket notation to traverse the path in an object:
<col-umn>{'<level1_element>'}{'<level2_element>'}.
D. Use dot notation to traverse a path in a JSON object:
<col-umn>:<level1_element>.<level2_element>.<level3_element>.

Answer: B,D

NEW QUESTION # 69
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats.
The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A. AWS Glue
B. AWS DataSync
C. Amazon S3 Transfer Acceleration
D. AWS Direct Connect

Answer: B

Explanation:
AWS DataSync is a managed data transfer service that simplifies and accelerates moving large amounts of data online between on-premises storage and Amazon S3, EFS, or FSx for Windows File Server. DataSync is optimized for efficient, incremental, and reliable transfers of large datasets, making it suitable for transferring 5 TB of data with daily updates.

NEW QUESTION # 70
......

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DEA-C01 FREE EXAM, NEW DEA-C01 EXAM PATTERN

DEA-C01 Free Exam, New DEA-C01 Exam Pattern