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Databricks Certified Data Analyst Associate Exam Sample Questions (Q30-Q35):
NEW QUESTION # 30
A data engineer is working with a nested array column products in table transactions. They want to expand the table so each unique item in products for each row has its own row where the transaction_id column is duplicated as necessary.
They are using the following incomplete command:
Which of the following lines of code can they use to fill in the blank in the above code block so that it successfully completes the task?
- A. reduce(produces)
- B. array distinct(produces)
- C. flatten(produces)
- D. array(produces)
- E. explode(produces)
Answer: E
Explanation:
The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column's values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary. Reference: Databricks Documentation I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with "SELECT" indicating a query to retrieve data. "transaction_id," suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with "FROM transactions;" indicating data is being selected from a 'transactions' table.
If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:
Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where you can find the exam guide, registration details, and preparation tips.
Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.
Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.
Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.
NEW QUESTION # 31
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?
- A. The suppliers table now contains only the data from the new suppliers table.
- B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
- C. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
- D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
- E. The command fails because it is written incorrectly.
Answer: E
Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn
NEW QUESTION # 32
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. Reduce the SQL endpoint cluster size
- B. Turn off the Auto stop feature
- C. Increase the minimum scaling value
- D. Use a Serverless SQL endpoint
- E. Increase the SQL endpoint cluster size
Answer: D
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 # 33
Which of the following layers of the medallion architecture is most commonly used by data analysts?
- A. Gold
- B. All of these layers are used equally by data analysts
- C. None of these layers are used by data analysts
- D. Bronze
- E. Silver
Answer: A
Explanation:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. Reference: What is the medallion lakehouse architecture?
NEW QUESTION # 34
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 create two separate dashboards.
- B. They will need to add two separate visualizations to the dashboard based on the same Query.
- C. They will need to copy the Query and create one data visualization per query.
- D. They will need to decide on a single data visualization to add to the dashboard.
- E. They will need to alter the Query to return two separate sets of results.
Answer: B
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 # 35
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