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Databricks-Certified-Data-Analyst-Associate Valid Test Notes | Valid Exam Databricks-Certified-Data-Analyst-Associate Preparation

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

Topic
Details

Topic 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.

Topic 2

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 3

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 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.

>> Databricks-Certified-Data-Analyst-Associate Valid Test Notes <<

Valid Exam Databricks-Certified-Data-Analyst-Associate Preparation - Real Databricks-Certified-Data-Analyst-Associate Testing Environment

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

A. Use Workflows to establish a cluster for Fivetran to interact with
B. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
C. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
D. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
E. Use Delta Live Tables to establish a cluster for Fivetran to interact with

Answer: D

Explanation:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

NEW QUESTION # 44
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 ORDER BY. which is not allowed in an aggregation.
B. The query is using count('). which will count all the customers in the customers table, no matter the region.
C. The query is selecting region but region should only occur in the ORDER BY clause.
D. The query is missing a GROUP BY region clause.
E. There are no mistakes in the query.

Answer: D

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 # 45
Which of the following approaches can be used to ingest data directly from cloud-based object storage?

A. Create an external table while specifying the object storage path to LOCATION
B. Create an external table while specifying the DBFS storage path to FROM
C. It is not possible to directly ingest data from cloud-based object storage
D. Create an external table while specifying the DBFS storage path to PATH
E. Create an external table while specifying the object storage path to FROM

Answer: A

Explanation:
External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
AI-generated code. Review and use carefully. More info on FAQ.

NEW QUESTION # 46
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?

A. Data blending
B. Last-mile
C. Last-mile ETL
D. Data enhancement
E. Data testing

Answer: C

Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. Reference: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL

NEW QUESTION # 47
A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.
Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?

A. Increase the minimum scaling value
B. Use a Serverless SQL endpoint
C. Turn off the Auto stop feature
D. Reduce the SQL endpoint cluster size
E. Increase the SQL endpoint cluster size

Answer: B

Explanation:
A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster. Reference: Databricks SQL endpoints, Serverless SQL endpoints, SQL endpoint clusters

NEW QUESTION # 48
......

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