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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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.
Topic 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.
Topic 4	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.
Topic 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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Databricks Certified Data Analyst Associate Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.
B. All data visualizations and counters must be created using Queries.
C. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
D. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.

Answer: B

Explanation:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
Develop Queries:
For each desired visualization or counter, write a SQL query that retrieves the necessary data.
Create Visualizations and Counters:
After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.
Assemble the Dashboard:
Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.

NEW QUESTION # 88
A data engineer is working with a nested array column products in table transactions. They want to expand the table so each unique item in products for each row has its own row where the transaction_id column is duplicated as necessary.
They are using the following incomplete command:

Which of the following lines of code can they use to fill in the blank in the above code block so that it successfully completes the task?

A. flatten(produces)
B. array distinct(produces)
C. array(produces)
D. reduce(produces)
E. explode(produces)

Answer: E

Explanation:
The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column's values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary. Reference: Databricks Documentation I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with "SELECT" indicating a query to retrieve data. "transaction_id," suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with "FROM transactions;" indicating data is being selected from a 'transactions' table.
If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:
Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where you can find the exam guide, registration details, and preparation tips.
Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.
Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.
Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.

NEW QUESTION # 89
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. Remove the trials=trials argument
B. Reduce num_evals to be less than 10
C. Remove the algo=tpe.suggest argument
D. Change fmin() to fmax()
E. Change SparkTrials() to Trials()

Answer: E

Explanation:
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.

NEW QUESTION # 90
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?

A. Line Chart
B. Histogram
C. There is no default. The user must choose a visualization type.
D. Violin Chart
E. IBar Chart

Answer: E

Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types

NEW QUESTION # 91
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?

A. Create a table via a connection between Databricks and the desktop facilitated by Partner Connect.
B. Create a table by uploading the file to cloud storage and then importing the data to Databricks.
C. Create a table by manually copying and pasting the data values into cloud storage and then importing the data to Databricks.
D. Create a table by uploading the file using the Create page within Databricks SQL

Answer: D

Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:
* Access the Data Upload Interface:
* In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
* Select Create or modify a table.
* Upload the CSV File:
* Click on the browse button or drag and drop the CSV file directly onto the designated area.
* The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.
* Configure Table Settings:
* After uploading, a preview of the data is displayed.
* Specify the table name, select the appropriate schema, and configure any additional settings as needed.
* Create the Table:
* Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.
Reference: Create or modify a table using file upload

NEW QUESTION # 92
......

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