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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Analyst Associate Exam |
| Exam Number: | Databricks-Certified-Data-Analyst-Associate |
| Related Certifications: | Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45–60 |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Select |
| Passing Score: | 70% |
| 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 # 55
Which example of data projects represents a common analytics application to be completed in Databricks SQL?
Answer: A
Explanation:
Option B is correct. A data analyst commonly uses Databricks SQL for analytics-focused transformations, aggregation, joining, filtering, and preparation of datasets for reporting or dashboarding. Analysis-specific ETL on Gold-layer tables is a "last-mile" analytics task: the Gold layer is already curated for business use, and the analyst performs additional project-specific refinement. The official Databricks exam guide includes creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and using dashboards as Data Analyst Associate skills. It also states that Gold-layer data drives downstream analytics and dashboards. Options A, C, and D are primarily streaming engineering, machine learning, and MLOps activities, not common Databricks SQL analytics applications. References: Databricks Certified Data Analyst Associate Exam Guide and medallion architecture documentation.
NEW QUESTION # 56
A business analyst has been asked to create a data entity/object called sales_by_employee. It should always stay up-to-date when new data are added to the sales table. The new entity should have the columns sales_person, which will be the name of the employee from the employees table, and sales, which will be all sales for that particular sales person. Both the sales table and the employees table have an employee_id column that is used to identify the sales person.
Which of the following code blocks will accomplish this task?
Answer: A
Explanation:
The SQL code provided in Option D is the correct way to create a view named sales_by_employee that will always stay up-to-date with the sales and employees tables. The code uses the CREATE OR REPLACE VIEW statement to define a new view that joins the sales and employees tables on the employee_id column. It selects the employee_name as sales_person and all sales for each employee, ensuring that the data entity
/object is always up-to-date when new data are added to these tables.
The answer can be verified from Databricks SQL documentation which provides insights on creating views using SQL queries, joining tables, and selecting specific columns to be included in the view. Reference link: Databricks SQL
NEW QUESTION # 57
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:
Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?
Answer: E
Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks
NEW QUESTION # 58
A data analyst is troubleshooting a query in Databricks SQL that fails when processing large datasets and complex join operations. Logs indicate that the job consistently aborts due to resource constraint errors on the cluster.
Which Query Profile metric should the analyst use to identify the operator that is causing resource overuse?
Answer: B
Explanation:
The correct answer is C because the issue is a resource constraint failure, and the analyst needs to identify which operator is consuming excessive memory. In Query Profile, Memory peak shows memory usage at the operator level and helps identify the operator causing resource overuse. Time spent helps identify slow operators, shuffle read size helps analyze data movement, and bytes spilled to disk indicates spill behavior, but the most direct metric for resource overuse due to memory pressure is memory peak.
Official documentation extract used: Databricks Query Profile graph view shows metrics such as "Time spent, Memory peak, and Rows."
NEW QUESTION # 59
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which command could the data engineering team use to access sales in PySpark?
Answer: D
Explanation:
Option B is correct because spark.table( " sales " ) returns the named table as a Spark DataFrame, which the data engineering team can then test using PySpark. SELECT * FROM sales is SQL text, not a PySpark command by itself. spark.sql( " sales " ) is invalid because spark.sql expects a SQL statement, not just a table name. spark.delta.table is not the standard PySpark API for loading a table. Official Databricks extract:
DataFrameReader.table "returns the specified table as a DataFrame," and Databricks also lists spark.table as a Spark operation that returns a DataFrame.
NEW QUESTION # 60
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