FREE PDF 2025 DATABRICKS RELIABLE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER: DATABRICKS CERTIFIED PROFESSIONAL DATA ENGINEER EXAM STUDY MATERIALS

Free PDF 2025 Databricks Reliable Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Study Materials

Free PDF 2025 Databricks Reliable Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Study Materials

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Databricks Certified Professional Data Engineer certification exam is a rigorous and challenging exam that requires a deep understanding of data engineering concepts and the Databricks platform. Candidates must have a strong foundation in computer science and data engineering, as well as practical experience using the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises that test a candidate's ability to design, build, and maintain data pipelines using the Databricks platform.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which of the following SQL commands are used to append rows to an existing delta table?

  • A. INSERT INTO table_name
  • B. APPEND INTO table_name
  • C. COPY DELTA INTO table_name
  • D. UPDATE table_name
  • E. APPEND INTO DELTA table_name

Answer: A

Explanation:
Explanation
The answer is INSERT INTO table_name
Insert adds rows to an existing table, this is very similar to add rows a traditional Database or Da-tawarehouse.


NEW QUESTION # 72
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

  • A. Integer programming
  • B. Apriori Algorithm
  • C. Ridge and Lasso
  • D. Ordinary Least squares

Answer: D

Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters


NEW QUESTION # 73
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

  • A. "Can Manage" privileges on the required cluster
  • B. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
  • C. "Can Restart" privileges on the required cluster
  • D. Cluster creation allowed. "Can Restart" privileges on the required cluster
  • E. Cluster creation allowed. "Can Attach To" privileges on the required cluster

Answer: E

Explanation:
Explanation
This is the minimal permission a user would need to start and attach to an already configured cluster. Cluster creation allowed means that the user can create new clusters or start existing clusters that are stopped. "Can Attach To" privileges on the required cluster means that the user can attach notebooks or libraries to that cluster and run commands on it. Verified References: Databricks Certified Data Engineer Professional, under
"Security & Governance" section; Databricks Documentation, under "Cluster permissions" section.


NEW QUESTION # 74
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

  • A. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
  • B. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
  • C. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
  • D. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
  • E. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

Answer: D

Explanation:
Explanation
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.


NEW QUESTION # 75
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

  • A. withWatermark("event_time", "10 minutes")
  • B. slidingWindow("event_time", "10 minutes")
  • C. awaitArrival("event_time", "10 minutes")
  • D. await("event_time + '10 minutes'")
  • E. delayWrite("event_time", "10 minutes")

Answer: A

Explanation:
Explanation
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The otheroptions are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html


NEW QUESTION # 76
......

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