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

主題	簡介
主題 1	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 2	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.
主題 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.
主題 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.
主題 5	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.

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最新的 Data Analyst Databricks-Certified-Data-Analyst-Associate 免費考試真題 (Q99-Q104):

問題 #99
A Data Analyst is working on sensor_df; this DataFrame contains two columns: record_datetime timestamp and record array.
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?

A. Uses withColumn, but selects sensor_id, status, and health as if they are already top-level columns.
B. exploded_df = exploded_df.select( " record_datetime " , " record_exploded " )
C. Selects nested fields before correctly creating the exploded column.
D. exploded_df = sensor_df.withColumn( " record_exploded " , explode( " record " ))exploded_df = exploded_df.select( " record_datetime " , " record_exploded.sensor_id " , " record_exploded.status " , " record_exploded.health " )

答案：D

解題說明：
Option C is correct after correcting the formatting and typing errors in the uploaded option text. The analyst needs explode( " record " ) because the record column is an array, and the requirement is to return one array item per row. Then the analyst must select fields from the exploded struct using dot notation, such as record_exploded.sensor_id, record_exploded.status, and record_exploded.health. Databricks PySpark documentation states that explode "returns a new row for each element in the given array or map," and withColumn returns a new DataFrame by adding or replacing a column. The select method projects expressions or column names into the resulting DataFrame.

問題 #100
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to successfully complete before the new task begins.
B. When another task has the same dependency libraries as the new task.
C. When another task needs to fail before the new task begins.
D. When another task needs to use as little compute resources as possible.
E. When another task needs to be replaced by the new task.

答案：A

解題說明：
Option E is correct. The Depends On field is used to define task dependencies in a Databricks job, so a downstream task waits for an upstream task according to the dependency rules. The standard use case is when one task must complete successfully before the next task starts. Official Databricks extract: "You can control the execution order of tasks by specifying dependencies between them," and Databricks runs upstream tasks before downstream tasks.

問題 #101
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Databricks Filesystem
B. Jobs
C. Repos
D. Dashboards
E. Data Explorer

答案：E

解題說明：
Option E is correct. In the current Databricks UI, Data Explorer is known as Catalog Explorer. It is the location used to browse catalogs, schemas, tables, and views, review object metadata, and request or inspect access-related information. DBFS, Jobs, Dashboards, and Repos are not the table-permission review location.
Official Databricks extract: users can navigate the catalog tree, open an object's page, and request additional privileges from Catalog Explorer.

問題 #102
A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function, and they have defined the search space search_space.
As a result, they have the following code block:
num_evals = 100
trials = SparkTrials()
best_hyperparam = fmin(
fn=objective_function,
space=search_space,
algo=tpe.suggest,
max_evals=num_evals,
trials=trials
)
Which of the following changes do they need to make to the above code block in order to accomplish the task?

A. Reduce num_evals to be less than 10
B. Remove the algo=tpe.suggest argument
C. Change fmin() to fmax()
D. Remove the trials=trials argument
E. Change SparkTrials() to Trials()

答案：E

解題說明：
Option A is correct. The model being tuned is a Spark ML model, which is already distributed. SparkTrials is intended to distribute independent single-machine trials across Spark workers. For distributed ML algorithms such as Spark MLlib/Spark ML, Hyperopt should run trials from the driver so each trial can access the full cluster resources. Therefore, SparkTrials() should be changed to Trials(). Official Databricks documentation explains that this setup works for distributed machine learning algorithms including Apache Spark MLlib, and the Databricks notebook guidance states that SparkTrials is incompatible for that distributed-training pattern because each trial must be evaluated on the driver node.

問題 #103
What is used as a compute resource for Databricks SQL?

A. Standard clusters
B. Single-node clusters
C. SQL warehouses
D. Downstream BI tools integrated with Databricks SQL

答案：C

問題 #104
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