DEA-C01 Testing Engine & DEA-C01 Prüfungs-Guide

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Snowflake DEA-C01 Exam Overview:

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

>> DEA-C01 Testing Engine <<

DEA-C01 Prüfungs-Guide & DEA-C01 Vorbereitungsfragen

Wenn Sie finden, dass eine große Herausforderung in Ihrem Berufsleben vor Ihnen steht, so müssen Sie die Snowflake DEA-C01 Zertifizierungsprüfung bestehen. PrüfungFrage ist eine echte Website, die umfassende Kenntnisse zur Snowflake DEA-C01 Zertifizierungsprüfung besitzt. Wir bieten exklusive Online-Snowflake DEA-C01 Prüfungsfragen und Antworten. So ist es ganz leicht, die Prüfung zu bestehen. Unser PrüfungFrage bietet Ihnen 100%-Pass-Garantie. PrüfungFrage ist als Anführer der professionalen Zertifizierung anerkannt. Sie bietet die umfangreichste Zertifizierungsantworten. Sie werden feststellen, dass die Snowflake DEA-C01 Prüfungsfragen und Antworten zur Zeit die gründlichste, genaueste und neueste Praxis sind. Wenn Sie die Snowflake DEA-C01 Prüfungsfragen und Antworten haben, werden Sie sicher mehr sicher sein, die Prüfung zum ersten Mal zu bestehen.

Snowflake DEA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
Thema 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.
Thema 4	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.
Thema 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam DEA-C01 Prüfungsfragen mit Lösungen (Q134-Q139):

134. Frage
Mark the correct statements about Cache?

A. Warehouse cache is dropped when the warehouse is suspended, which may result in slower initial performance for some queries after the warehouse is resumed.
B. The size of the warehouse cache is determined by the compute resources in the ware-house.
C. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
D. For persisted query results of all sizes, the cache expires after 24 hours.
E. Materialized views are more flexible than, but typically slower than, cached results.

Antwort: A,B,C,D,E

Begründung:
Explanation
How Does Warehouse Caching Impact Queries?
Each warehouse, when running, maintains a cache of table data accessed as queries are processed by the warehouse. This enables improved performance for subsequent queries if they are able to read from the cache instead of from the table(s) in the query. The size of the cache is determined by the compute resources in the warehouse (i.e. the larger the warehouse and, therefore, more compute re-sources in the warehouse), the larger the cache.
This cache is dropped when the warehouse is suspended, which may result in slower initial perfor-mance for some queries after the warehouse is resumed. As the resumed warehouse runs and pro-cesses more queries, the cache is rebuilt, and queries that are able to take advantage of the cache will experience improved performance.
Keep this in mind when deciding whether to suspend a warehouse or leave it running. In other words, consider the trade-off between saving credits by suspending a warehouse versus maintaining the cache of data from previous queries to help with performance.
Using Persisted Query Results
When a query is executed, the result is persisted (i.e. cached) for a period of time. At the end of the time period, the result is purged from the system.
Snowflake uses persisted query results to avoid re-generating results when nothing has changed (i.e. "retrieval optimization"). In addition, you can use persisted query results to post-process the results (e.g. layering a new query on top of the results already calculated).
For persisted query results of all sizes, the cache expires after 24 hours.
Both materialized views and cached query results provide query performance benefits:
Materialized views are more flexible than, but typically slower than, cached results.
Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
Regular views do not cache data, and therefore cannot improve performance by caching.

135. Frage
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. A materialized view speeds both equality searches and range 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. She can go for clustering on underlying tables which can speedup Equality searches.

Antwort: B

Begründung:
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.

136. Frage
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)

A. Use Amazon Redshift as a file storage system to archive old inventory management reports.
B. Use Amazon Redshift ML to generate inventory recommendations.
C. Use SQL to invoke a remote SageMaker endpoint for prediction.
D. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
E. Use Amazon Redshift ML to schedule regular data exports for offline model training.

Antwort: B,C

Begründung:
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift.
Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.

137. Frage
A company needs to use an Aws Glue PySpark job to read specific data from an Amazon DynamoDB table. The company knows the partition key values for the required records. The existing processing logic of the AWS Glue PySpark job requires the data to be in DynamicFrame format. The company needs a solution to ensure that the job reads only the specified data. Which solution will meet this requirement with the MINIMUM number of read capacity units (RCUs)?

A. Perform a scan on the DynamoDB table in the Aws Glue job. Put the data into a DynamicFrame.
Filter the DynamicFrame on the partition key.
B. Use the AWS Glue DynamoDB ETL connector to read the DynamoDB table. Use the filter option to read the required partition key.
C. Perform a query on the DynamoDB table in the AWS Glue job. Use the partition key in the key condition expression. Put the data into a DynamicFrame.
D. Perform a query on the DynamoDB table in the AWS Glue job by using only the sort key in the key condition expression. Load the data into a DynamicFrame.

Antwort: C

Begründung:
A DynamoDB Query that uses the partition key in the key condition expression reads only the items in the specified partition(s), which minimizes consumed RCUs compared with any Scan- based approach. The job can then convert the query results into a DynamicFrame to satisfy the existing processing logic.

138. Frage
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Use Athena partition projection based on the S3 bucket prefix.
B. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.
C. Transform the data that is in the S3 bucket to Apache Parquet format.
D. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.
E. Create an AWS Glue partition index. Enable partition filtering.

Antwort: A,E

Begründung:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren't using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions' metadata to the AWS Glue table.

139. Frage
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DEA-C01 Prüfungs-Guide: https://www.pruefungfrage.de/DEA-C01-dumps-deutsch.html

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