Databricks-Certified-Data-Analyst-Associate最新考古題 & Databricks-Certified-Data-Analyst-Associate試題

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Databricks Databricks-Certified-Data-Analyst-Associate 考試大綱：

主題	簡介
主題 1	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.
主題 2	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.
主題 3	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.
主題 4	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.
主題 5	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.

>> Databricks-Certified-Data-Analyst-Associate最新考古題 <<

Databricks-Certified-Data-Analyst-Associate最新考古題 - 你通過Databricks Certified Data Analyst Associate Exam的強大武器

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最新的 Data Analyst Databricks-Certified-Data-Analyst-Associate 免費考試真題 (Q71-Q76):

問題 #71
Which statement describes descriptive statistics?

A. A branch of statistics that uses summary statistics to quantitatively 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 summary statistics to categorically describe and summarize data.
D. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

答案：A

問題 #72
Which of the following statements describes descriptive statistics?

A. A branch of statistics that uses summary statistics to quantitatively 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 summary statistics to categorically describe and summarize data.
D. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
E. A branch of statistics that uses quantitative variables that must take on an uncountable set of values.

答案：A

解題說明：
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

問題 #73
In a healthcare provider organization using Delta Lake to store electronic health records, a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied.
What approach should the Data Engineer take to allow the analyst to query that specific prior version?

A. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.
B. Restore the table to the version from two weeks ago using the RESTORE command, and have the analyst query the restored table.
C. Identify the version number corresponding to two weeks ago from the Delta transaction log, share that version number with the analyst to query using VERSION AS OF syntax, or export that version to a new Delta table for the analyst to query.
D. Use the VACUUM command to remove all versions of the table older than two weeks, then the analyst can query the remaining version.

答案：C

解題說明：
Option B is correct. Delta Lake supports time travel, which allows users to query previous table versions by timestamp or version number. The version can be obtained from table history and queried using VERSION AS OF. Restoring the table would change the current table state for everyone, which is not necessary when the analyst only needs to analyze a historical snapshot. VACUUM would remove old data files and can prevent time travel. Official Databricks extract: time travel supports "querying previous table versions," and the syntax includes SELECT * FROM people10m VERSION AS OF 123. Databricks also states that the version can be obtained from DESCRIBE HISTORY. Note: the two-week snapshot is queryable only if the required log and data files are still retained.

問題 #74
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

A. Owner account information
B. None of these
C. Table metadata, data summary visualizations, and owner account information
D. Table metadata
E. Data summary visualizations

答案：D

解題說明：
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
* Table schema
* Partitioning information
* Data file paths
* Transactional operations like inserts, updates, and deletes
* Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.
Reference: Delta Lake Table Features - Databricks Documentation

問題 #75
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could only run the entire program on Sundays.
B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
C. They could redesign the data model to separate the data used in the final query into a new table.
D. They could submit a feature request with Databricks to add this functionality.
E. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.

答案：E

解題說明：
Option B is correct. The program can be scheduled to run daily as a Databricks job, and Python control flow can determine whether the final query runs only when the current day is Sunday. Databricks jobs support scheduled execution, and PySpark notebooks can run SQL through Spark while using Python logic around those commands. Official Databricks extract: jobs can be configured "to run it on a time-based schedule," and spark.sql/Spark DataFrame operations allow Python-based workflows to interact with SQL data.

問題 #76
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Databricks-Certified-Data-Analyst-Associate試題: https://tw.fast2test.com/Databricks-Certified-Data-Analyst-Associate-premium-file.html

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