DEA-C01 REAL EXAM QUESTIONS - NEW DEA-C01 EXAM BOOK

DEA-C01 Real Exam Questions - New DEA-C01 Exam Book

DEA-C01 Real Exam Questions - New DEA-C01 Exam Book

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Tags: DEA-C01 Real Exam Questions, New DEA-C01 Exam Book, Test DEA-C01 Sample Online, Valid DEA-C01 Test Registration, DEA-C01 Best Preparation Materials

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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 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.
Topic 5
  • 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
When would a Data engineer use table with the flatten function instead of the lateral flatten combination?

  • A. Whenthe LATERALFLATTENcombination requires no other source m the from clause to refer to
  • B. When TABLE with FLATTENrequires another source in the from clause to refer to
  • C. When table withFLATTENis acting like a sub-query executed for each returned row
  • D. WhenTABLE with FLATTENrequires no additional source m the from clause to refer to

Answer: B

Explanation:
Explanation
The TABLE function with the FLATTEN function is used to flatten semi-structured data, such as JSON or XML, into a relational format. The TABLE function returns a table expression that can be used in the FROM clause of a query. The TABLE function with the FLATTEN function requires another source in the FROM clause to refer to, such as a table, view, or subquery that contains the semi-structured data. For example:
SELECT t.value:city::string AS city, f.value AS population FROM cities t, TABLE(FLATTEN(input => t.value:population)) f; In this example, the TABLE function with the FLATTEN function refers to the cities table in the FROM clause, which contains JSON data in a variant column named value. The FLATTEN function flattens the population array within each JSON object and returns a table expression with two columns: key and value.
The query then selects the city and population values from the table expression.


NEW QUESTION # 77
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?

  • A. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
  • B. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
  • C. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.
  • D. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.

Answer: A

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/tutorial-add-crawler.html


NEW QUESTION # 78
A marketing company collects clickstream data. The company sends the clickstream data to Amazon Kinesis Data Firehose and stores the clickstream data in Amazon S3. The company wants to build a series of dashboards that hundreds of users from multiple departments will use.
The company will use Amazon QuickSight to develop the dashboards. The company wants a solution that can scale and provide daily updates about clickstream activity.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

  • A. Access the query data through QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine). Configure a daily refresh for the dataset.
  • B. Use Amazon Athena to query the clickstream data
  • C. Use Amazon S3 analytics to query the clickstream data.
  • D. Use Amazon Redshift to store and query the clickstream data.
  • E. Access the query data through a QuickSight direct SQL query.

Answer: A,B

Explanation:
Athena would be cheaper than Redshift. S3 analytics is irrelevant. The functionality in SPICE should be more cost effective than direct SQL by reducing the frequency and volume of queries.


NEW QUESTION # 79
Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.

  • A. The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
  • B. Query results contain results that require significant processing.
  • C. The view's base table change frequently.
  • D. Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).

Answer: C

Explanation:
Explanation
A materialized view is a pre-computed data set derived from a query specification (the SELECT in the view definition) and stored for later use. Because the data is pre-computed, querying a material-ized view is faster than executing a query against the base table of the view. This performance dif-ferencecan be significant when a query is run frequently or is sufficiently complex. As a result, ma-terialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets.
Materialized views require Enterprise Edition.
Materialized views are designed to improve query performance for workloads composed of com-mon, repeated query patterns. However, materializing intermediate results incurs additional costs. As such, before creating any materialized views, you should consider whether the costs are offset by the savings from re-using these results frequently enough.
Materialized views are particularly useful when:
Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
Query results contain results that require significant processing, including:
1. Analysis of semi-structured data.
2. Aggregates that take a long time to calculate.
The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
The view's base table does not change frequently.


NEW QUESTION # 80
A Data Engineer would like to define a file structure for loading and unloading data Where can the file structure be defined? (Select THREE)

  • A. pipe object
  • B. stage object
  • C. copy command
  • D. MERGE command
  • E. INSERT command
  • F. FILE FORMAT Object

Answer: B,C,F

Explanation:
Explanation
The places where the file format can be defined are copy command, file format object, and stage object. These places allow specifying or referencing a file format that defines how data files are parsed and loaded into or unloaded from Snowflake tables. A file format can include various options, such as field delimiter, field enclosure, compression type, date format, etc. The other options are not places where the file format can be defined. Option B is incorrect because MERGE command is a SQL command that can merge data from one table into another based on a join condition, but it does not involve loading or unloading data files. Option D is incorrect because pipe object is a Snowflake object that can load data from an external stage into a Snowflake table using COPY statements, but it does not define or reference a file format. Option F is incorrect because INSERT command is a SQL command that can insert data into a Snowflake table from literal values or subqueries, but it does not involve loading or unloading data files.


NEW QUESTION # 81
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