DEA-C01 Dumps Download | Practice DEA-C01 Exams

The SnowPro Advanced: Data Engineer Certification Exam exam is one of the most valuable certification exams. The Snowflake SnowPro Advanced: Data Engineer Certification Exam exam opens a door for beginners or experienced Itcerttest professionals to enhance in-demand skills and gain knowledge. DEA-C01 Exam credential is proof of candidates' expertise and knowledge. After getting success in the Snowflake SnowPro Advanced: Data Engineer Certification Exam exam, candidates can put their careers on the fast route and achieve their goals in a short period of time.

Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
Partition columns optimize query performance by pruning out the data files that do not need to be scanned (i.e.
partitioning the external table). Which pseudocolumn of External table evaluate as an expression that parses the path and/or filename information.

A. METADATA$ROW_NUMBER
B. METADATA$COLUMNNAME
C. METADATA$FILEPATH
D. METADATA$FILENAME

Answer: D

Explanation:
Explanation
METADATA$FILENAME
A pseudocolumn that identifies the name of each staged data file included in the external table, in-cluding its path in the stage.
An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the parti-tion column.

NEW QUESTION # 97
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
B. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
C. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

Answer: D

Explanation:
Amazon Managed Service for Apache Flink provides real-time stream processing capabilities, which can process sensor data with low latency. By using Apache Flink connectors, the processed data can be efficiently written to Amazon Timestream, which is optimized for time- series data storage and querying.

NEW QUESTION # 98
Alex, a Data Engineer with one of the Data analytics Organization, created the Materialized view over External tables to improve Data Reporting Experience.
Step 1: He created materialized view named DataReportMV
1.create or replace materialized view DataReportMV as
2.select Item_id, Item_price from Items;
Step 2: He joined a materialized view with a sales table as
1.create or replace view Revenue as
2.select m.item_id, sum(ifnull(s.quantity, 0)) as quantity,
3.sum(ifnull(quantity * (s.price - m.item_price), 0)) as profit
4.from DataReportMV as m left outer join sales as s on s.item_id = m.item_id
5.group by m.item_id;
Step 3: After 1 hour, he decided to temporarily suspend the use (and maintenance) of the DataRe-portMV materialized view for cost saving purpose.
alter materialized view DataReportMV suspend;
Please select what Alex is doing wrong here?

A. Materialized view on top of External tables is not supported feature.
B. Alex is doing everything correct.
C. There is no command like suspend for temporarily suspension of Materialized views, Step 3 will give error like invalid Suspend command.
D. Once DataReportMV got suspended , any query on the top of the view will generate er-ror like:
Failure during expansion of view 'DATAREPORTMV': SQL compilation error: Material-ized view DataReportMV is invalid.
E. A materialized view, DataReportMV does not support Join operations, so Step 2 would be failed & he cannot proceed further.

Answer: B

Explanation:
Explanation
All Steps will be executed successfully by Alex without any error.

NEW QUESTION # 99
Which of the following statements is/are incorrect regarding Fail-safe data recovery?

A. Data stored in temporary tables is not recoverable after the table is dropped as they do not have fail-safe.
B. If downtime and the time required to reload lost data are factors, permanent tables, even with their added Fail-safe costs, may offer a better overall solution than transient tables.
C. Historical data in transient tables can be recovered by Snowflake due to Operation fail-ure after the Time Travel retention period ends using Fail-safe.
D. Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.
E. Long-lived tables, such as fact tables, should always be defined as permanent to ensure they are fully protected by Fail-safe.

Answer: C

NEW QUESTION # 100
Regular views do not cache data, and therefore cannot improve performance by caching?

A. TRUE
B. FALSE

Answer: A

Explanation:
Explanation
Regular views do not cache data, and therefore cannot improve performance by caching.

NEW QUESTION # 101
......

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DEA-C01 Dumps Download | Practice DEA-C01 Exams

Snowflake DEA-C01 Exam Syllabus Topics:

Practice DEA-C01 Exams - DEA-C01 Dumps Torrent

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q96-Q101):

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