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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Analyst Associate Exam |
| Exam Number: | Databricks-Certified-Data-Analyst-Associate |
| Real Exam Qty: | 45–60 |
| Available Languages: | English |
| Exam Price: | $200 USD |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Duration: | 90 minutes |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Databricks SQL Training Courses Databricks Academy - Data Analyst Learning Path |
| Exam Registration: | Databricks Certification Portal Kryterion Webassessor Registration |
| Sample Questions: | Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions |
| Exam Way: | Online proctored exam via remote monitoring (Kryterion Webassessor platform) |
| Pre Condition: | No formal prerequisites required, but familiarity with SQL and basic data analysis concepts is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
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NEW QUESTION # 87
How can a data analyst determine if query results were pulled from the cache?
Answer: E
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. References: 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 # 88
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?
Answer: C
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. References: 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 # 89
A data analyst has been asked to provide a list of options on how to share a dashboard with a client. It is a security requirement that the client does not gain access to any other information, resources, or artifacts in the database.
Which of the following approaches cannot be used to share the dashboard and meet the security requirement?
Answer: A
Explanation:
The approach that cannot be used to share the dashboard and meet the security requirement is D. Generating a Personal Access Token that is good for 1 day and sharing it with the client. This approach would give the client access to the Databricks workspace using the token owner's identity and permissions, which could expose other information, resources, or artifacts in the database1. The other approaches can be used to share the dashboard and meet the security requirement because:
* A. Downloading the Dashboard as a PDF and sharing it with the client would only provide a static snapshot of the dashboard without any interactive features or access to the underlying data2.
* B. Setting a refresh schedule for the dashboard and entering the client's email address in the
"Subscribers" box would send the client an email with the latest dashboard results as an attachment or a link to a secure web page3. The client would not be able to access the Databricks workspace or the dashboard itself.
* C. Taking a screenshot of the dashboard and sharing it with the client would also only provide a static snapshot of the dashboard without any interactive features or access to the underlying data4.
* E. Downloading a PNG file of the visualizations in the dashboard and sharing them with the client would also only provide a static snapshot of the visualizations without any interactive features or access to the underlying data5. References:
* 1: Personal access tokens
* 2: Download as PDF
* 3: Automatically refresh a dashboard
* 4: Take a screenshot
* 5: Download a PNG file
NEW QUESTION # 90
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table;
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?
Answer: D
Explanation:
An external table is a table that is defined in the metastore, but its data is stored outside of the Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the metadata is deleted from the metastore, but the data files are not affected. This is different from a managed table, which is a table whose data is stored in the Databricks environment, and whose data files are deleted when the table is dropped. To delete the data files of an external table, the analyst needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from the storage system. Reference: DROP TABLE, Drop Delta table features, Best practices for dropping a managed Delta Lake table
NEW QUESTION # 91
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:
After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?
Answer: C
Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
* Views do not store data themselves but reference data from underlying tables.
* Logging out or being inactive does not delete or alter views.
* Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
* Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.
eur_customers, and it will return the same data (provided the underlying table hasn't changed).
Reference: Views - Databricks Documentation
NEW QUESTION # 92
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