EXAM DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE TRAINING - DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE VALID STUDY QUESTIONS

Exam Databricks-Certified-Data-Analyst-Associate Training - Databricks-Certified-Data-Analyst-Associate Valid Study Questions

Exam Databricks-Certified-Data-Analyst-Associate Training - Databricks-Certified-Data-Analyst-Associate Valid Study Questions

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

  • A. Automated alerting systems
  • B. Advanced dashboarding capabilities
  • C. Simultaneous multi-user support
  • D. Computations, data, and analytical tools on the same platform

Answer: D

Explanation:
Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.


NEW QUESTION # 56
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart.
Which of the following statements is true?

  • A. The area chart will use whatever is selected in the Dashboard Parameter while all or the other visualizations will remain changed regardless of their parameter use.
  • B. The area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
  • C. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
  • D. The area chart will use whatever value is input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
  • E. The area chart will convert to a Dashboard Parameter.

Answer: B

Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. Reference: Databricks SQL dashboards, Query parameters


NEW QUESTION # 57
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

  • A. They will need to add two separate visualizations to the dashboard based on the same Query.
  • B. They will need to decide on a single data visualization to add to the dashboard.
  • C. They will need to create two separate dashboards.
  • D. They will need to copy the Query and create one data visualization per query.
  • E. They will need to alter the Query to return two separate sets of results.

Answer: A

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL


NEW QUESTION # 58
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 DBFS storage path to PATH
  • B. Create an external table while specifying the object storage path to LOCATION
  • C. Create an external table while specifying the DBFS storage path to FROM
  • D. Create an external table while specifying the object storage path to FROM
  • E. It is not possible to directly ingest data from cloud-based object storage

Answer: B

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 # 59
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

  • A. Owner account information
  • B. Table metadata, data summary visualizations, and owner account information
  • C. Data summary visualizations
  • D. None of these
  • E. Table metadata

Answer: E

Explanation:
Delta Lake stores table data as a series of data files in a specified location, but it also stores table metadata in a transaction log. The table metadata includes the schema, partitioning information, table properties, and other configuration details. The table metadata is stored alongside the data files and is updated atomically with every write operation. The table metadata can be accessed using the DESCRIBE DETAIL command or the DeltaTable class in Scala, Python, or Java. The table metadata can also be enriched with custom tags or user-defined commit messages using the TBLPROPERTIES or userMetadata options. Reference:
Enrich Delta Lake tables with custom metadata
Delta Lake Table metadata - Stack Overflow
Metadata - The Internals of Delta Lake


NEW QUESTION # 60
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