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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Section	Objectives
Data Management in Lakehouse	- Data ingestion and preparation 1. Batch and streaming ingestion concepts - Delta Lake fundamentals 1. Table optimization concepts 2. ACID transactions and versioning
Data Visualization and Dashboards	- Creating dashboards in Databricks SQL 1. Dashboard configuration and sharing 2. Visualizing query results
Data Governance and Security	- Access control and permissions 1. Unity Catalog basics 2. Row-level and column-level security concepts
Databricks SQL and SQL Analytics	- Querying data using SQL in Databricks 1. SELECT statements and filtering data 2. Joins and aggregations - Data transformation and analysis 1. Window functions 2. Common table expressions (CTEs)

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
A data analyst has been asked to use the below table sales_table to get the percentage rank of products within region by the sales:

The result of the query should look like this:

Which of the following queries will accomplish this task?
A)

B)

C)

D)

A. Option A
B. Option C
C. Option B
D. Option D

Answer: C

Explanation:
The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:
* A. Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
* C. Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function.
The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
* D. Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function.
The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). References:
* 1: PERCENT_RANK (Transact-SQL)
* 2: Window functions in Databricks SQL
* 3: Databricks Certified Data Analyst Associate Exam Guide

NEW QUESTION # 71
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level dat
a. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

A. The streaming data is not an appropriate data source for a dashboard
B. The streaming cluster is not fault tolerant
C. The required compute resources could be costly
D. The dashboard cannot be refreshed that quickly
E. The gold-level tables are not appropriately clean for business reporting

Answer: C

Explanation:
A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B . The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C . The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D . The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E . The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. Reference: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

NEW QUESTION # 72
In which of the following situations will the mean value and median value of variable be meaningfully different?

A. When the variable contains no outliers
B. When the variable contains a lot of extreme outliers
C. When the variable contains no missing values
D. When the variable is of the categorical type
E. When the variable is of the boolean type

Answer: B

Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. References: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

NEW QUESTION # 73
A data analyst wants the following output:
customer_name number_of_orders
John Doe 388
Zhang San 234
Which statement will produce this output?

A. SELECT customerjiame, count(order_id)
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id GROUP BY customerjiame;
B. SELECT customerjiame, (order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id;
C. SELECT customer_name, count(order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id USE customer_name;
D. SELECT customer_name, count(order_id) AS number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id
GROUP BY customer_name;

Answer: D

NEW QUESTION # 74
Which location can be used to determine the owner of a managed table?

A. Review the Owner field in the schema page using Data Explorer
B. Review the Owner field in the table page using Catalog Explorer
C. Review the Owner field in the table page using the SQL Editor
D. Review the Owner field in the database page using Data Explorer

Answer: B

Explanation:
In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:
* Access Catalog Explorer:
* In your Databricks workspace, click on the Catalog icon in the sidebar to open Catalog Explorer.
* Navigate to the Table:
* Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.
* View Table Details:
* Click on the table name to open its details page.
* Identify the Owner:
* On the table ' s details page, review the Owner field, which displays the principal (user, service principal, or group) that owns the table.
This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.
Reference: Manage Unity Catalog object ownership

NEW QUESTION # 75
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