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The SnowPro Advanced: Data Engineer Certification Exam certification has become very popular to survive in today's difficult job market in the technology industry. Every year, hundreds of Snowflake aspirants attempt the DEA-C01 exam since passing it results in well-paying jobs, salary hikes, skills validation, and promotions. Lack of Real DEA-C01 Exam Questions is their main obstacle during DEA-C01 certification test preparation.
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 65 |
| Exam Price: | $375 USD |
| Passing Score: | 750 / 1000 |
| Exam Format: | Multiple select, Multiple choice |
| Available Languages: | English, Japanese |
| Exam Duration: | 115 minutes |
| Related Certifications: | SnowPro Advanced: Data Analyst SnowPro Advanced: Data Scientist SnowPro Advanced: Security Engineer SnowPro Advanced: Architect SnowPro Core SnowPro Advanced: Administrator |
| Recommended Training: | SnowPro Advanced: Data Engineer Exam Study Guide Snowflake University Training Courses |
| 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/ |
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NEW QUESTION # 86
Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.
Answer: D
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 # 87
Snowpipe API provides a REST endpoint for defining the list of files to ingest that Informs Snow-flake about the files to be ingested into a table. A successful response from this endpoint means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. What is name of this Endpoint?
Answer: B
Explanation:
Explanation
The Snowpipe API provides a REST endpoint for defining the list of files to ingest.
Endpoint: insertFiles
Informs Snowflake about the files to be ingested into a table. A successful response from this end-point means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. For more details, see the response codes below.
In most cases, Snowflake inserts fresh data into the target table within a few minutes.
To Know more about SnowFlake Rest API used for Data File ingestion, do refer:
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-rest-apis.html#data-file-ingestion
NEW QUESTION # 88
In one of your created Schema, you have been required to create Internal Stages, what are the In-correct considerations you can noticed from the below options? [Select All that Apply]
Answer: C
Explanation:
Explanation
A stage specifies where data files are stored (i.e. "staged") so that the data in the files can be loaded into a table.
Types of Internal Stages
User Stages
Table Stages
Named Stages
By default, each user and table in Snowflake is automatically allocated an internal stage for staging data files to be loaded. In addition, you can create named internal stages.
File staging information is required during both steps in the data loading process:
You must specify an internal stage in the PUT command when uploading files to Snowflake.
You must specify the same stage in the COPY INTO <table> command when loading data into a table from the staged files.
Consider the best type of stage for specific data files. Each option provides benefits and potential drawbacks.
User Stages
Each user has a Snowflake stage allocated to them by default for storing files. This stage is a con-venient option if your files will only be accessed by a single user, but need to be copied into multi-ple tables.
User stages have the following characteristics and limitations:
User stages are referenced using @~; e.g. use LIST @~ to list the files in a user stage.
Unlike named stages, user stages cannot be altered or dropped.
User stages do not support setting file format options. Instead, you must specify file format and copy options as part of the COPY INTO <table> command.
This option is not appropriate if:
Multiple users require access to the files.
The current user does not have INSERT privileges on the tables the data will be loaded into.
Table Stages
Each table has a Snowflake stage allocated to it by default for storing files. This stage is a conven-ient option if your files need to be accessible to multiple users and only need to be copied into a sin-gle table.
Table stages have the following characteristics and limitations:
Table stages have the same name as the table; e.g. a table named mytable has a stage referenced as
@%mytable.
Unlike named stages, table stages cannot be altered or dropped.
Table stages do not support transforming data while loading it (i.e. using a query as the source for the COPY command).
Note that a table stage is not a separate database object; rather, it is an implicit stage tied to the table itself. A table stage has no grantable privileges of its own. To stage files to a table stage, list the files, query them on the stage, or drop them, you must be the table owner (have the role with the OWNERSHIP privilege on the table).
This option is not appropriate if you need to copy the data in the files into multiple tables.
Named Stages
Named stages are database objects that provide the greatest degree of flexibility for data loading:
Users with the appropriate privileges on the stage can load data into any table.
Because the stage is a database object, the security/access rules that apply to all objects apply. The privileges to use a stage can be granted or revoked from roles. In addition, ownership of the stage can be transferred to another role.
If you plan to stage data files that will be loaded only by you, or will be loaded only into a single table, then you may prefer to simply use either your user stage or the stage for the table into which you will be loading data.
Named stages are optional but recommended when you plan regular data loads that could involve multiple users and/or tables.
NEW QUESTION # 89
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: C
Explanation:
AWS Glue workflows and data quality actions allow for seamless integration with AWS Glue to ensure that data quality checks are automatically performed after ETL jobs. This solution leverages the existing AWS Glue infrastructure and EventBridge to trigger notifications without needing complex orchestration tools like Apache Airflow or EMR, resulting in lower operational overhead.
Amazon EventBridge can easily be configured to trigger a notification to Amazon SNS when a dataset is incomplete, providing a highly automated and low-maintenance solution.
Airflow requires managing a separate cluster and configuring Directed Acyclic Graphs (DAGs), which introduces more operational complexity and overhead than AWS Glue workflows.
Using Amazon EMR introduces unnecessary complexity and higher operational overhead, as it requires provisioning and managing a cluster for Spark jobs, which is more complex than using AWS Glue and EventBridge.
While Lambda can be a lightweight option, orchestrating Lambda functions for data quality checks with Step Functions adds unnecessary complexity when AWS Glue workflows already offer built-in capabilities for data quality actions with lower overhead.
NEW QUESTION # 90
Data Engineer identified use case where he decided to use materialized view for query perfor-mance. Which one is not the limitation he must be aware of before using MVs in their use case?
Answer: A,F
Explanation:
Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.
NEW QUESTION # 91
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