PASS GUARANTEED 2025 LATEST DEA-C01: SNOWPRO ADVANCED: DATA ENGINEER CERTIFICATION EXAM KNOWLEDGE POINTS

Pass Guaranteed 2025 Latest DEA-C01: SnowPro Advanced: Data Engineer Certification Exam Knowledge Points

Pass Guaranteed 2025 Latest DEA-C01: SnowPro Advanced: Data Engineer Certification Exam Knowledge Points

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Tags: DEA-C01 Knowledge Points, DEA-C01 Valid Exam Tips, Latest DEA-C01 Exam Registration, Reliable DEA-C01 Exam Blueprint, DEA-C01 Practice Test

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The Snowflake DEA-C01 practice exam material is available in three different formats i.e Snowflake DEA-C01 dumps PDF format, web-based practice test software, and desktop DEA-C01 practice exam software. PDF format is pretty much easy to use for the ones who always have their smart devices and love to prepare for DEA-C01 Exam from them. Applicants can also make notes of printed SnowPro Advanced: Data Engineer Certification Exam (DEA-C01) exam material so they can use it anywhere in order to pass Snowflake DEA-C01 Certification with a good score.

Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.
Topic 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.
Topic 4
  • 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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which ones are the false statements about Materialized Views?

  • A. Clustering a subset of the materialized views on a table tends to be more cost-effective than clustering the table itself.
  • B. A materialized view can also be used as the data source for a subquery.
  • C. Snowflake does not allow users to truncate materialized views.
  • D. Materialized views can be secure views.
  • E. Materialized views are first-class account objects.
  • F. Snowflake does not allow standard DML (e.g. INSERT, UPDATE, DELETE) on ma-terialized views.

Answer: E

Explanation:
Explanation
Materialized views are first-class Database objects & rest of the understandings are true.


NEW QUESTION # 69
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

  • A. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
  • B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
  • C. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
  • D. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.

Answer: C


NEW QUESTION # 70
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. A materialized view speeds both equality searches and range searches.
  • C. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
  • D. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
    (Correct)

Answer: D

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 # 71
A Data Engineer needs to ingest invoice data in PDF format into Snowflake so that the data can be queried and used in a forecasting solution.
..... recommended way to ingest this data?

  • A. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
  • B. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data
  • C. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
  • D. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data

Answer: D

Explanation:
Explanation
The recommended way to ingest invoice data in PDF format into Snowflake is to create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data. This option allows for more flexibility and control over how the PDF data is extracted and transformed. The other options are not suitable for ingesting PDF data into Snowflake. Option A and B are incorrect because Snowpipe and COPY INTO commands can only ingest files that are in supported file formats, such as CSV, JSON, XML, etc. PDF files are not supported by Snowflake and will cause errors or unexpected results.
Option C is incorrect because external tables can only query files that are in supported file formats as well.
PDF files cannot be parsed by external tables and will cause errors or unexpected results.


NEW QUESTION # 72
Which of the following System keeps the following characteristics?
a. It will keep in it all the raw data.
b. Generally, the users of it is data scientists and data developers.
c. Flat architecture
d. Highly agile

  • A. Data Hub
  • B. Data Warehouse
  • C. Data Mart
  • D. Data Lake

Answer: D


NEW QUESTION # 73
......

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