SUREPASSEXAMS REAL DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER QUESTIONS PDF

SurePassExams Real Databricks Databricks-Certified-Professional-Data-Engineer Questions PDF

SurePassExams Real Databricks Databricks-Certified-Professional-Data-Engineer Questions PDF

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Databricks Certified Professional Data Engineer certification exam is a highly sought-after certification in the data engineering industry. It is designed to test the skills and knowledge of data engineers who work with Databricks, a cloud-based platform that helps organizations manage large amounts of data and perform advanced analytics.

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To take the Databricks Certified Professional Data Engineer certification exam, candidates must have a solid understanding of data engineering concepts, as well as experience using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks, which require candidates to demonstrate their ability to perform specific data engineering tasks using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

  • A. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
  • B. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
  • C. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
  • D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
  • E. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.

Answer: B

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.


NEW QUESTION # 22
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. await("event_time + '10 minutes'")
  • B. withWatermark("event_time", "10 minutes")
  • C. slidingWindow("event_time", "10 minutes")
  • D. awaitArrival("event_time", "10 minutes")
  • E. delayWrite("event_time", "10 minutes")

Answer: B

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 other options 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 # 23
What type of table is created when you create delta table with below command?
CREATE TABLE transactions USING DELTA LOCATION "DBFS:/mnt/bronze/transactions"

  • A. Temp table
  • B. Delta Lake table
  • C. Managed delta table
  • D. External table
  • E. Managed table

Answer: D

Explanation:
Explanation
Anytime a table is created using the LOCATION keyword it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/" format -> DELTA, JSON, CSV, PARQUET, TEXT I created the table command based on the above question, you can see it created an external table,


NEW QUESTION # 24
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

  • A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
  • B. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
  • C. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
  • D. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
  • E. The write will fail completely because of the constraint violation and no records will be inserted into the target table.

Answer: E

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints: https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions


NEW QUESTION # 25
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

  • A. Df.apply(model, columns). Select (''customer_id, prediction''
  • B. Model, predict (df, columns)
  • C. Df. Select (''customer_id''.
    Model (''columns) alias (''predictions'')
  • D. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

Answer: B

Explanation:
Given the information that the model is registered with MLflow and assumingpredictis the method used to apply the model to a set of columns, we use themodel.predict()function to apply the model to the DataFrame dfusing the specifiedcolumns. Themodel.predict()function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictionscolumn.
When working with PySpark, this predictions column needs to be selected alongside thecustomer_idto create a new DataFrame with the schemacustomer_id LONG, predictions DOUBLE.
References:
* MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.
html#python-function-python
* PySpark MLlib documentation on model prediction:https://spark.apache.org/docs/latest/ml-pipeline.
html#pipeline


NEW QUESTION # 26
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