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Real Databricks Databricks-Certified-Data-Analyst-Associate Exam Questions in PDF Format

The latest Databricks-Certified-Data-Analyst-Associate exam prep is created by our IT experts and certified trainers who are dedicated to Databricks braindumps pdf for a long time. All questions of our Databricks-Certified-Data-Analyst-Associate PDF VCE are written based on the real questions. Besides, we always check the updating of Databricks-Certified-Data-Analyst-Associate exam questions to make sure exam preparation smoothly.

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

Topic
Details

Topic 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.

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

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.

Topic 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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

A. A quantitative variable that can take on an uncountable set of values
B. A quantitative variable that never stops changing
C. A quantitative variable Chat can take on a finite or countably infinite set of values
D. A categorical variable in which the number of categories continues to increase over time

Answer: A

Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

NEW QUESTION # 66
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

A. Data deletion
B. Flexible schemas
C. Scalable storage
D. Open-source formats
E. ACID transactions

Answer: E

Explanation:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide

NEW QUESTION # 67
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

A. DROP TABLE table_name FROM database_name;
B. DELETE TABLE table_name FROM database_name;
C. DROP DATABASE database_name;
D. DELETE TABLE database_name.table_name;
E. DROP TABLE database_name.table_name;

Answer: E

Explanation:
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. Reference: Databricks - DROP TABLE

NEW QUESTION # 68
A data analyst has been asked to count the number of customers in each region and has written the following query:

If there is a mistake in the query, which of the following describes the mistake?

A. The query is using count('). which will count all the customers in the customers table, no matter the region.
B. The query is missing a GROUP BY region clause.
C. The query is using ORDER BY. which is not allowed in an aggregation.
D. The query is selecting region but region should only occur in the ORDER BY clause.
E. There are no mistakes in the query.

Answer: B

Explanation:
In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the "GROUP BY" clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. Reference: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.

NEW QUESTION # 69
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
B. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
C. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
D. All files with the .customers extension are deleted.
E. Running SELECT * FROM accounts.customers will return all rows in the table.

Answer: B

Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

NEW QUESTION # 70
......

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