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

Topic	Details
Topic 1	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 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.
Topic 3	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 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	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.
Which of the following projects should be completed in Databricks SQL?

A. Segmenting customers into like groups using a clustering algorithm
B. Automating complex notebook-based workflows with multiple tasks
C. Tracking usage of feature variables for machine learning projects
D. Combining two data sources into a single, comprehensive dataset
E. Testing the quality of data as it is imported from a source

Answer: D

Explanation:
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3.
This can help simplify the data management and governance, as well as improve the data quality and consistency. References:
* Databricks SQL overview
* Databricks SQL use cases
* Joining data sources

NEW QUESTION # 46
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?

A. dates_df.withColumn( " date " , f.date_format( " timestamp " , " yyyy-MM-dd " )).show()
B. dates_df.withColumn( " date " , f.unix_timestamp( " timestamp " )).show()
C. dates_df.withColumn( " date " , f.from_unixtime( " timestamp " )).show()
D. dates_df.withColumn( " date " , f.to_date( " timestamp " )).show()

Answer: D

Explanation:
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.

NEW QUESTION # 47
In which of the following situations will the mean value and median value of variable be meaningfully different?

A. When the variable is of the boolean type
B. When the variable contains no missing values
C. When the variable contains a lot of extreme outliers
D. When the variable is of the categorical type
E. When the variable contains no outliers

Answer: C

Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. Reference: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

NEW QUESTION # 48
In which of the following situations should a data analyst use higher-order functions?

A. When built-in functions need to run through the Catalyst Optimizer
B. When custom logic needs to be applied at scale to array data objects
C. When custom logic needs to be converted to Python-native code
D. When custom logic needs to be applied to simple, unnested data
E. When built-in functions are taking too long to perform tasks

Answer: B

Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

NEW QUESTION # 49
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 Chat can take on a finite or countably infinite set of values
B. A categorical variable in which the number of categories continues to increase over time
C. A quantitative variable that never stops changing
D. A quantitative variable that can take on an uncountable set of values

Answer: D

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 # 50
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