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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 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	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	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 (Q100-Q105):

NEW QUESTION # 100
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 object storage path to FROM
C. Create an external table while specifying the DBFS storage path to PATH
D. Create an external table while specifying the DBFS storage path to FROM
E. It is not possible to directly ingest data from cloud-based object storage

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.
External tables

NEW QUESTION # 101
A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. Copy the Query and create one data visualization per query.
B. Add two separate visualizations to the dashboard based on the same Query.
C. Decide on a single data visualization to add to the dashboard.
D. Alter the Query to return two separate sets of results.

Answer: B

Explanation:
Databricks SQL allows you to create multiple visualizations from a single query result. These visualizations can be customized independently and each can be added to a dashboard. This feature is explicitly supported and recommended in Databricks' documentation on dashboards and visualization workflows, enabling flexible reporting without duplicating queries.

NEW QUESTION # 102
Query History provides Databricks SQL users with a lot of benefits. A data analyst has been asked to share all of these benefits with their team as part of a training exercise. One of the benefit statements the analyst provided to their team is incorrect.
Which statement about Query History is incorrect?

A. It can be used to view the query plan of queries that have run.
B. It can be used to automate query execution on multiple warehouses (formerly endpoints).
C. It can be used to debug queries.
D. It can be used to troubleshoot slow running queries.

Answer: B

Explanation:
Query History in Databricks SQL is intended for reviewing executed queries, understanding their execution plans, and identifying performance issues or errors for debugging purposes. It allows users to analyze query duration, resources used, and potential bottlenecks. However, Query History does not provide any capability to automate the execution of queries across multiple warehouses; automation must be handled through jobs or external orchestration tools, not through the Query History feature itself.

NEW QUESTION # 103
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
B. Queries that use query parameters cannot be used with Alerts.
C. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
D. Queries that return results based on dates cannot be used with Alerts.
E. Alerts don't work with queries that access tables.

Answer: B

Explanation:
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.

NEW QUESTION # 104
How can a data analyst determine if query results were pulled from the cache?

A. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
B. Go to the Alerts tab and check the Cache Status alert.
C. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
D. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
E. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.

Answer: C

Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

NEW QUESTION # 105
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