High Pass-Rate Databricks-Certified-Professional-Data-Engineer Certification Training Provide Prefect Assistance in Databricks-Certified-Professional-Data-Engineer Preparation

You can easily use the PDF format on your tablets, laptops, and smartphones. It means you can save your free time and read Actual Databricks-Certified-Professional-Data-Engineer PDF Questions from any place. So, get PDF questions, study it properly and have faith in yourself. You can reach new heights and prove yourself to those who used to think that you are not worth competing with them.

Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer Exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.

Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their expertise in using the Databricks platform. It provides employers with a way to identify and verify the skills of candidates and employees, and it can help data engineers advance their careers by demonstrating their proficiency in using the Databricks platform to build and maintain scalable and reliable data pipelines.

>> Databricks-Certified-Professional-Data-Engineer Certification Training <<

Databricks Databricks-Certified-Professional-Data-Engineer Reliable Test Prep | Reliable Databricks-Certified-Professional-Data-Engineer Exam Online

The Databricks Practice Test engine included with Databricks-Certified-Professional-Data-Engineer exam questions simulates the actual Databricks-Certified-Professional-Data-Engineer examinations. This is excellent for familiarizing yourself with the Databricks Certified Professional Data Engineer Exam and learning what to expect on test day. You may also use the Databricks Databricks-Certified-Professional-Data-Engineer online practice test engine to track your progress and examine your answers to determine where you need to improve on the Databricks-Certified-Professional-Data-Engineer exam.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q87-Q92):

NEW QUESTION # 87
The data engineering team is using a bunch of SQL queries to review data quality and monitor the ETL job every day, which of the following approaches can be used to set up a schedule and auto-mate this process?

A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
B. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
C. They can schedule the query to run every 1 day from the Jobs UI
D. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
E. They can schedule the query to run every 12 hours from the Jobs UI.

Answer: B

Explanation:
Explanation
Explanation
Individual queries can be refreshed on a schedule basis,
To set the schedule:
1. Click the query info tab.
Graphical user interface, text, application, email Description automatically generated

* Click the link to the right of Refresh Schedule to open a picker with schedule intervals.
Graphical user interface, application Description automatically generated

* Set the schedule.
The picker scrolls and allows you to choose:
* An interval: 1-30 minutes, 1-12 hours, 1 or 30 days, 1 or 2 weeks
* A time. The time selector displays in the picker only when the interval is greater than 1 day and the day selection is greater than 1 week. When you schedule a specific time, Databricks SQL takes input in your computer's timezone and converts it to UTC. If you want a query to run at a certain time in UTC, you must adjust the picker by your local offset. For example, if you want a query to execute at 00:00 UTC each day, but your current timezone is PDT (UTC-7), you should select 17:00 in the picker:
Graphical user interface Description automatically generated

* Click OK.
Your query will run automatically.
If you experience a scheduled query not executing according to its schedule, you should manually trigger the query to make sure it doesn't fail. However, you should be aware of the following:
* If you schedule an interval-for example, "every 15 minutes"-the interval is calculated from the last successful execution. If you manually execute a query, the scheduled query will not be executed until the interval has passed.
* If you schedule a time, Databricks SQL waits for the results to be "outdated". For example, if you have a query set to refresh every Thursday and you manually execute it on Wednesday, by Thursday the results will still be considered "valid", so the query wouldn't be scheduled for a new execution. Thus, for example, when setting a weekly schedule, check the last query execution time and expect the scheduled query to be executed on the selected day after that execution is a week old. Make sure not to manually execute the query during this time.
If a query execution fails, Databricks SQL retries with a back-off algorithm. The more failures the further away the next retry will be (and it might be beyond the refresh interval).
Refer documentation for additional info,
https://docs.microsoft.com/en-us/azure/databricks/sql/user/queries/schedule-query

NEW QUESTION # 88
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, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
D. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')

Answer: B

Explanation:
Given the information that the model is registered with MLflow and assuming predict is the method used to apply the model to a set of columns, we use the model.predict() function to apply the model to the DataFrame df using the specified columns. The model.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 predictions column. When working with PySpark, this predictions column needs to be selected alongside the customer_id to create a new DataFrame with the schema customer_id LONG, predictions DOUBLE.
Reference:
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 # 89
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Answer: E

NEW QUESTION # 90
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
B. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
C. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
D. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
E. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

Answer: B

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

NEW QUESTION # 91
You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

A. GENERATED ALWAYS AS (CAST(orderTime as DATE))
Correct)
B. GENERATED DEFAULT AS (CAST(orderTime as DATE))
C. Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process
D. AS DEFAULT (CAST(orderTime as DATE))
E. AS (CAST(orderTime as DATE))

Answer: A

Explanation:
Explanation
The answer is, GENERATED ALWAYS AS (CAST(orderTime as DATE))
https://docs.microsoft.com/en-us/azure/databricks/delta/delta-batch#--use-generated-columns Delta Lake supports generated columns which are a special type of columns whose values are au-tomatically generated based on a user-specified function over other columns in the Delta table. When you write to a table with generated columns and you do not explicitly provide values for them, Delta Lake automatically computes the values.
Note: Databricks also supports partitioning using generated column

NEW QUESTION # 92
......

As is known to us, a suitable learning plan is very important for all people. For the sake of more competitive, it is very necessary for you to make a learning plan. We believe that our Databricks-Certified-Professional-Data-Engineer actual exam will help you make a good learning plan. You can have a model test in limited time by our Databricks-Certified-Professional-Data-Engineer Study Materials, if you finish the model test, our system will generate a report according to your performance. And in this way, you can have the best pass percentage on your Databricks-Certified-Professional-Data-Engineer exam.

Databricks-Certified-Professional-Data-Engineer Reliable Test Prep: https://www.real4dumps.com/Databricks-Certified-Professional-Data-Engineer_examcollection.html