시험준비에가장좋은Databricks-Certified-Data-Analyst-Associate인기자격증덤프문제덤프데모문제다운받기

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Databricks인증Databricks-Certified-Data-Analyst-Associate시험을 패스하여 자격증을 취득한다면 여러분의 미래에 많은 도움이 될 것입니다.Databricks인증Databricks-Certified-Data-Analyst-Associate시험자격증은 it업계에서도 아주 인지도가 높고 또한 알아주는 시험이며 자격증 하나로도 취직은 문제없다고 볼만큼 가치가 있는 자격증이죠.Databricks인증Databricks-Certified-Data-Analyst-Associate시험은 여러분이 it지식테스트시험입니다.

Databricks Databricks-Certified-Data-Analyst-Associate 시험요강:

주제	소개
주제 1	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
주제 2	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
주제 3	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
주제 4	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
주제 5	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.

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최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q56-Q61):

질문 # 56
A data analyst is working with gold-layer tables to complete an ad-hoc project. A stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use.
Which of the following terms is used to describe this data augmentation?

A. Last-mile
B. Ad-hoc improvements
C. Last-mile ETL
D. Data enhancement
E. Data testing

정답：D

설명：
Data enhancement is the process of adding or enriching data with additional information to improve its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data sources with new data sources, such as external datasets, synthetic data, or machine learning models. Data enhancement can help data analysts to gain deeper insights, discover new patterns, and solve complex problems. Data enhancement is one of the applications of generative AI, which can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated business-level tables that are typically organized in consumption-ready project-specific databases234. The gold-layer tables are the final layer of data transformations and data quality rules in the medallion lakehouse architecture, which is a data design pattern used to logically organize data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use. This means that the analyst can use the additional dataset to enhance the existing gold-layer tables with more information, such as new features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform | Databricks Blog What is the medallion lakehouse architecture? - Azure Databricks What is a Medallion Architecture? - Databricks Synthetic Data for Better Machine Learning | Databricks Blog

질문 # 57
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level dat
a. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

A. The streaming cluster is not fault tolerant
B. The streaming data is not an appropriate data source for a dashboard
C. The dashboard cannot be refreshed that quickly
D. The gold-level tables are not appropriately clean for business reporting
E. The required compute resources could be costly

정답：E

설명：
A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B . The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C . The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D . The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E . The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. Reference: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

질문 # 58
Which of the following statements describes descriptive statistics?

A. A branch of statistics that uses summary statistics to categorically describe and summarize data.
B. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
C. A branch of statistics that uses quantitative variables that must take on an uncountable set of values.
D. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
E. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

정답：D

설명：
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data. Reference: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL

질문 # 59
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

정답：B

질문 # 60
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to alter the Query to return two separate sets of results.
B. They will need to add two separate visualizations to the dashboard based on the same Query.
C. They will need to create two separate dashboards.
D. They will need to copy the Query and create one data visualization per query.
E. They will need to decide on a single data visualization to add to the dashboard.

정답：B

설명：
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

질문 # 61
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만약 아직도Databricks Databricks-Certified-Data-Analyst-Associate인증시험 위하여 많은 시간과 정력을 소모하며 열심히 공부하고 있습니까? 아직도 어덯게하면Databricks Databricks-Certified-Data-Analyst-Associate인증시험을 빠르게 취득할 수 있는 방법을 못찿고 계십니까? 지금DumpTOP에서Databricks Databricks-Certified-Data-Analyst-Associate인증시험을 안전하게 넘을 수 있도록 대책을 내드리겠습니다. 아주 신기한 효과가 있을 것입니다.

Databricks-Certified-Data-Analyst-Associate인증시험 덤프공부: https://www.dumptop.com/Databricks/Databricks-Certified-Data-Analyst-Associate-dump.html

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시험준비에가장좋은Databricks-Certified-Data-Analyst-Associate인기자격증덤프문제덤프데모문제다운받기

Databricks Databricks-Certified-Data-Analyst-Associate 시험요강:

Databricks-Certified-Data-Analyst-Associate인증시험 덤프공부 - Databricks-Certified-Data-Analyst-Associate덤프샘플문제 체험

최신 Data Analyst Databricks-Certified-Data-Analyst-Associate 무료샘플문제 (Q56-Q61):

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