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Databricks Certified Data Analyst Associate Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
B. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
C. Queries that return results based on dates cannot be used with Alerts.
D. Queries that use query parameters cannot be used with Alerts.
E. Alerts don't work with queries that access tables.

Answer: D

Explanation:
The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.
Here's why:
Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.
When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.
However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn't dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.
As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.
Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts cannot work with date-based queries at all. In fact, they can-as long as the query is static (i.e., without parameters).
Reference:
Databricks SQL Alerts Documentation
Databricks Knowledge: "You cannot use alerts with queries that contain parameters."

NEW QUESTION # 13
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

A. Use Workflows to establish a cluster for Fivetran to interact with
B. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
C. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
D. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
E. Use Delta Live Tables to establish a cluster for Fivetran to interact with

Answer: B

Explanation:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran

NEW QUESTION # 14
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

A. A quantitative variable that can take on an uncountable set of values
B. A categorical variable in which the number of categories continues to increase over time
C. A quantitative variable Chat can take on a finite or countably infinite set of values
D. A quantitative variable that never stops changing

Answer: A

Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

NEW QUESTION # 15
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

A. Legal requirements for the area in which the data was collected
B. Organization-specific best practices for Pll data
C. Legal requirements for the area in which the analysis is being performed
D. All of these considerations
E. None of these considerations

Answer: D

Explanation:
Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. Reference:
How to Use Databricks to Encrypt and Protect PII Data
Automating Sensitive Data (PII/PHI) Detection
Databricks Certified Data Analyst Associate

NEW QUESTION # 16
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 customer_name, count(order_id) number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id USE customer_name;
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) AS number_of_orders
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id
GROUP BY customer_name;
D. SELECT customerjiame, count(order_id)
FROM customers
JOIN orders
ON customers.customer_id = orders.customer_id GROUP BY customerjiame;

Answer: C

NEW QUESTION # 17
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