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Databricks Certified Data Engineer Associate Exam Sample Questions (Q268-Q273):

NEW QUESTION # 268
A data engineer wants to create a new table containing the names of customers who live in France.
They have written the following command:
CREATE TABLE customersInFrance
_____ AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (Pll).
Which line of code fills in the above blank to successfully complete the task?

A. COMMENT "Contains PIT
B. 0
C. TBLPROPERTIES PII
D. "COMMENT PII"

Answer: C

Explanation:
To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:
CREATE TABLE customersInFrance
USING DELTA
TBLPROPERTIES ('PII' = 'true')
AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
The TBLPROPERTIES ('PII' = 'true') line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.
References:Databricks documentation on Delta Lake: Delta Lake on Databricks

NEW QUESTION # 269
A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail. Which feature of Delta Live Tables meets this requirement?

A. Monitoring
B. Change Data Capture
C. Expectations
D. Error Handling

Answer: C

Explanation:
Delta Live Tables Expectations allow defining data quality rules. Records that fail the expectations (such as sensor readings outside valid ranges) can be dropped without stopping the streaming pipeline, ensuring continuous and accurate processing.

NEW QUESTION # 270
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

Answer: E

NEW QUESTION # 271
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain a less refined view of data than Silver tables.
B. Gold tables are more likely to contain more data than Silver tables.
C. Gold tables are more likely to contain valuable data than Silver tables.
D. Gold tables are more likely to contain truthful data than Silver tables.
E. Gold tables are more likely to contain aggregations than Silver tables.

Answer: E

NEW QUESTION # 272
A global retail company sells products across multiple categories (e.g., Electronics, Clothing) and regions (e.g., North, South, East, West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

Calculate the total sales amount for each product category and store the results in a new dataframe called category_sales.
What will generate the expected result of category_sales?

A. category_sales =
sales_df.groupBy("region").agg(sum("sales_amount").alias("total_sales_amount"))
B. category_sales =
sales_df.agg(sum("sales_amount").groupBy("category").alias("total_sales_amount"))
C. category_sales =
sales_df.sum("sales_amount").groupBy("category").alias("total_sales_amount"))
D. category_sales =
sales_df.groupBy("category").agg(sum("sales_amount").alias("total_sales_amount"))

Answer: D

Explanation:
The correct approach is to group by the product category and then aggregate the sum of sales_amount, aliasing it as total_sales_amount. This produces the expected totals: 500 for Electronics and 900 for Clothing.

NEW QUESTION # 273
......

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