Get Perfect Results with Premium Databricks-Certified-Professional-Data-Engineer Dumps Updated 60 Questions [Q27-Q48] – IT Certification Exam Questions & Answers

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Get Perfect Results with Premium Databricks-Certified-Professional-Data-Engineer Dumps Updated 60 Questions

Free Databricks-Certified-Professional-Data-Engineer Exam Study Guide for the NEW Dumps Test Engine

Databricks Certified Professional Data Engineer exam is a rigorous test that measures a candidate’s knowledge and skills in various areas of Databricks, including data engineering, data modeling, data integration, data processing, and data storage. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to solve real-world problems using Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to assess the candidate’s ability to design and implement scalable, reliable, and efficient data solutions that meet business requirements.

Databricks Certified Professional Data Engineer certification exam is designed to test the knowledge and skills of data engineers who work with Databricks. Databricks is a cloud-based platform that provides a unified analytics engine for big data processing and machine learning. It is used by data engineers to manage data pipelines, extract insights from data, and build machine learning models. Databricks Certified Professional Data Engineer Exam certification exam is a comprehensive assessment of the candidate’s ability to use Databricks effectively for data engineering tasks.

NEW QUESTION 27
A team member is leaving the team and he/she is currently the owner of the few tables, instead of transfering the ownership to a user you have decided to transfer the ownership to a group so in the future anyone in the group can manage the permissions rather than a single individual, which of the following commands help you accomplish this?

ALTER TABLE table_name OWNER to ‘group’

TRANSFER OWNER table_name to ‘group’

GRANT OWNER table_name to ‘group’*

ALTER OWNER ON table_name to ‘group’

GRANT OWNER On table_name to ‘group’

NEW QUESTION 28
Which of the following statements are true about a lakehouse?

Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads

Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads

Lakehouse does not support ACID

Lakehouse do not support SQL

Lakehouse supports Transactions

NEW QUESTION 29
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

Setup an additional job to run ahead of the actual job so the cluster is running second job starts

Use the Databricks cluster pools feature to reduce the startup time

Use Databricks Premium edition instead of Databricks standard edition

Pin the cluster in the cluster UI page so it is always available to the jobs

Disable auto termination so the cluster is always running

NEW QUESTION 30
You are asked to debug a databricks job that is taking too long to run on Sunday’s, what are the steps you are going to take to identify the step that is taking longer to run?

A notebook activity of job run is only visible when using all-purpose cluster.

Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.

Enable debug mode in the Jobs to see the output activity of a job, output should be available to view.

Once a job is launched, you cannot access the job’s notebook activity.

Use the compute’s spark UI to monitor the job activity.

NEW QUESTION 31
identifies if its needs to be converted to Fahrenheit or Celcius with a one-word letter F or C?
select udf_convert(60,’C’) will result in 15.5
select udf_convert(10,’F’) will result in 50

1. CREATE UDF FUNCTION udf_convert(temp DOUBLE, measure STRING)
2. RETURNS DOUBLE
3. RETURN CASE WHEN measure == ‘F’ then (temp * 9/5) + 32
4. ELSE (temp – 33 ) * 5/9
5. END

1.CREATE UDF FUNCTION udf_convert(temp DOUBLE, measure STRING)
2. RETURN CASE WHEN measure == ‘F’ then (temp * 9/5) + 32
3. ELSE (temp – 33 ) * 5/9
4. END

1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURN CASE WHEN measure == ‘F’ then (temp * 9/5) + 32
3. ELSE (temp – 33 ) * 5/9
4. END

1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == ‘F’ then (temp * 9/5) + 32
4. ELSE (temp – 33 ) * 5/9
5. END
(Correct)

1.CREATE USER FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == ‘F’ then (temp * 9/5) + 32
4. ELSE (temp – 33 ) * 5/9
5.END

NEW QUESTION 32
Which of the following SQL statement can be used to query a table by eliminating duplicate rows from the query results?

SELECT DISTINCT * FROM table_name

SELECT DISTINCT * FROM table_name HAVING COUNT(*) > 1

SELECT DISTINCT_ROWS (*) FROM table_name

SELECT * FROM table_name GROUP BY * HAVING COUNT(*) < 1

SELECT * FROM table_name GROUP BY * HAVING COUNT(*) > 1

NEW QUESTION 33
You are currently working on a production job failure with a job set up in job clusters due to a data issue, what cluster do you need to start to investigate and analyze the data?

A Job cluster can be used to analyze the problem

All-purpose cluster/ interactive cluster is the recommended way to run commands and view the data.

Existing job cluster can be used to investigate the issue

Databricks SQL Endpoint can be used to investigate the issue

NEW QUESTION 34
Once a cluster is deleted, below additional actions need to performed by the administrator

Remove virtual machines but storage and networking are automatically dropped

Drop storage disks but Virtual machines and networking are automatically dropped

Remove networking but Virtual machines and storage disks are automatically dropped

Remove logs

No action needs to be performed. All resources are automatically removed.

NEW QUESTION 35
The viewupdatesrepresents an incremental batch of all newly ingested data to be inserted or updated in the customerstable.
The following logic is used to process these records.

Which statement describes this implementation?

The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

NEW QUESTION 36
Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?

1. MERGE INTO my_table a
2. USING new_records b;

1. SELECT *
2. FROM my_table
3. WHERE duplicate = False;

1. SELECT DISTINCT *
2. FROM my_table;

1. MERGE INTO my_table a
2. USING new_records b ON a.id = b.id
3. WHEN NOT MATCHED
4. THEN INSERT *;

1. DROP DUPLICATES
2. FROM my_table;

NEW QUESTION 37
Create a sales database using the DBFS location ‘dbfs:/mnt/delta/databases/sales.db/’

CREATE DATABASE sales FORMAT DELTA LOCATION ‘dbfs:/mnt/delta/databases/sales.db/”

CREATE DATABASE sales USING LOCATION ‘dbfs:/mnt/delta/databases/sales.db/’

CREATE DATABASE sales LOCATION ‘dbfs:/mnt/delta/databases/sales.db/’

The sales database can only be created in Delta lake

CREATE DELTA DATABASE sales LOCATION ‘dbfs:/mnt/delta/databases/sales.db/’

NEW QUESTION 38
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes an average of 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion?

Setup an additional job to run ahead of the actual job so the cluster is running second job starts

Use the Databricks cluster pools feature to reduce the startup time

Use Databricks Premium edition instead of Databricks standard edition

Pin the cluster in the cluster UI page so it is always available to the jobs

Disable auto termination so the cluster is always running

NEW QUESTION 39
You were asked to setup a new all-purpose cluster, but the cluster is unable to start which of the following steps do you need to take to identify the root cause of the issue and the reason why the cluster was unable to start?

Check the cluster driver logs

Check the cluster event logs
(Correct)

Workspace logs

Storage account

Data plane

NEW QUESTION 40
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?

Delete branch

Trigger Databricks CICD pipeline

Commit and push code

Create a pull request

Approve the pull request

NEW QUESTION 41
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

Improves the quality of your data

Validates a complete use case of your application

Troubleshooting is easier since all steps are isolated and tested individually

Yields faster deployment and execution times

Ensures that all steps interact correctly to achieve the desired end result

NEW QUESTION 42
If you create a database sample_db with the statement CREATE DATABASE sample_db what will be the default location of the database in DBFS?

Default location, DBFS:/user/

Default location, /user/db/

Default Storage account

Statement fails “Unable to create database without location”

Default Location, dbfs:/user/hive/warehouse

NEW QUESTION 43
Which of the following is a true statement about the global temporary view?

A global temporary view is available only on the cluster it was created, when the cluster restarts global temporary view is automatically dropped.

A global temporary view is available on all clusters for a given workspace

A global temporary view persists even if the cluster is restarted

A global temporary view is stored in a user database

A global temporary view is automatically dropped after 7 days

NEW QUESTION 44
Which of the following scenarios is the best fit for the AUTO LOADER solution?

Efficiently process new data incrementally from cloud object storage

Incrementally process new streaming data from Apache Kafa into delta lake

Incrementally process new data from relational databases like MySQL

Efficiently copy data from data lake location to another data lake location

Efficiently move data incrementally from one delta table to another delta table

Explanation
The answer is, Efficiently process new data incrementally from cloud object storage.
Please note: AUTO LOADER only works on data/files located in cloud object storage like S3 or Azure Blob Storage it does not have the ability to read other data sources, although AU-TO LOADER is built on top of structured streaming it only supports files in the cloud object stor-age. If you want to use Apache Kafka then you can just use structured streaming.
Diagram Description automatically generated

Auto Loader and Cloud Storage Integration
Auto Loader supports a couple of ways to ingest data incrementally
1.Directory listing – List Directory and maintain the state in RocksDB, supports incremental file listing
2.File notification – Uses a trigger+queue to store the file notification which can be later used to retrieve the file, unlike Directory listing File notification can scale up to millions of files per day.
[OPTIONAL]
Auto Loader vs COPY INTO?
Auto Loader
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a new Structured Streaming source called cloudFiles. Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory.
When to use Auto Loader instead of the COPY INTO?
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*You do not plan to load subsets of previously uploaded files. With Auto Loader, it can be more difficult to reprocess subsets of files. However, you can use the COPY INTO SQL command to reload subsets of files while an Auto Loader stream is simultaneously running.
Refer to more documentation here,
https://docs.microsoft.com/en-us/azure/databricks/ingestion/auto-loader

NEW QUESTION 45
You are currently working with the application team to setup a SQL Endpoint point, once the team started consuming the SQL Endpoint you noticed that during peak hours as the number of concur-rent users increases you are seeing degradation in the query performance and the same queries are taking longer to run, which of the following steps can be taken to resolve the issue?

They can turn on the Serverless feature for the SQL endpoint.

They can increase the maximum bound of the SQL endpoint’s scaling range.

They can increase the cluster size(2X-Small to 4X-Large) of the SQL endpoint.

They can turn on the Auto Stop feature for the SQL endpoint.

They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy from
“Cost optimized” to “Reliability Optimized.”

NEW QUESTION 46
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.

The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.

An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.

An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

NEW QUESTION 47
The data engineering team is looking to add a new column to the table, but the QA team would like to test the change before implementing in production, which of the below options allow you to quickly copy the table from Prod to the QA environment, modify and run the tests?

DEEP CLONE

SHADOW CLONE

ZERO COPY CLONE

SHALLOW CLONE

METADATA CLONE

NEW QUESTION 48
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

All records are cached to an operational database and then the filter is applied

The Parquet file footers are scanned for min and max statistics for the latitude column

All records are cached to attached storage and then the filter is applied

The Delta log is scanned for min and max statistics for the latitude column

The Hive metastore is scanned for min and max statistics for the latitude column

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