[Q28-Q46] Try Databricks-Certified-Professional-Data-Engineer Free Now! Real Exam Question Answers Updated [Aug 26, 2022]

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Try Databricks-Certified-Professional-Data-Engineer Free Now! Real Exam Question Answers Updated [Aug 26, 2022]

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Q28. A data engineer has developed a code block to perform a streaming read on a data source. The code block is
below:
1. (spark
2. .read
3. .schema(schema)
4. .format(“cloudFiles”)
5. .option(“cloudFiles.format”, “json”)
6. .load(dataSource)
7. )
The code block is returning an error.
Which of the following changes should be made to the code block to configure the block to successfully
perform a streaming read?

The .read line should be replaced with .readStream

The .format(“cloudFiles”) line should be replaced with .format(“stream”)

A new .stream line should be added after the spark line

A new .stream line should be added after the .read line

A new .stream line should be added after the .load(dataSource) line

Q29. Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

Q30. Which of the following data workloads will utilize a Silver table as its source?

A job that aggregates cleaned data to create standard summary statistics

A job that queries aggregated data that already feeds into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

A job that enriches data by parsing its timestamps into a human-readable format

A job that cleans data by removing malformatted records

Q31. A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing

All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminated

All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stopped

Q32. A data engineer has set up a notebook to automatically process using a Job. The data engineer’s manager wants
to version control the schedule due to its complexity.
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of
the Job’s schedule?

They can download the JSON description of the Job from the Job’s page

They can submit the Job once on an all-purpose cluster

They can link the Job to notebooks that are a part of a Databricks Repo

They can submit the Job once on a Job cluster

They can download the XML description of the Job from the Job’s page

Q33. A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?

They can observe the task as it runs to try and determine why it is failing

They can utilize a Jobs cluster for each of the tasks in the Job

They can institute a retry policy for the task that periodically fails

They can set up the Job to run multiple times ensuring that at least one will complete

They can institute a retry policy for the entire Job

Q34. Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?

The ability to write pipelines in Python and/or SQL

The ability to declare and maintain data table dependencies

The ability to automatically scale compute resources

The ability to access previous versions of data tables

The ability to perform batch and streaming queries

Q35. A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above
format regardless of whether a table already exists with this name?

1. CREATE OR REPLACE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT USING DELTA

1. CREATE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT

1. CREATE TABLE IF NOT EXISTS table_name ( id STRING, birthDate DATE, avgRating FLOAT )

1. CREATE OR REPLACE TABLE table_name
2. WITH COLUMNS ( id STRING, birthDate DATE, avgRating FLOAT ) USING DELTA

1. CREATE OR REPLACE TABLE table_name ( id STRING, birthDate DATE, avgRating FLOAT )

Q36. A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job’s most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?

They can set up an Alert for the query to notify when the ELT job fails

They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
than 60

They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
minutes

They can set up an Alert for the query to notify them if the returned value is greater than 60

This type of alerting is not possible in Databricks

Q37. A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?

A data lakehouse requires very little data modeling

A data lakehouse combines compute and storage for simple governance

A data lakehouse fully exists in the cloud

A data lakehouse stores unstructured data and is ACID-compliant

A data lakehouse provides autoscaling for compute clusters

Q38. Which of the following data workloads will utilize a Bronze table as its source?

A job that queries aggregated data to publish key insights into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

A job that enriches data by parsing its timestamps into a human-readable format

A job that develops a feature set for a machine learning application

A job that aggregates cleaned data to create standard summary statistics

Q39. Which of the following statements describes Delta Lake?

Delta Lake is an open source platform to help manage the complete machine learning lifecycle

Delta Lake is an open format storage layer that delivers reliability, security, and per-formance

Delta Lake is an open source data storage format for distributed data

Delta Lake is an open source analytics engine used for big data workloads

Delta Lake is an open format storage layer that processes data

Q40. Which of the following describes a scenario in which a data engineer will want to use a Job cluster instead of
an all-purpose cluster?

An ad-hoc analytics report needs to be developed while minimizing compute costs

A data engineer needs to manually investigate a production error

An automated workflow needs to be run every 30 minutes

A data team needs to collaborate on the development of a machine learning model

A Databricks SQL query needs to be scheduled for upward reporting

Q41. An engineering manager uses a Databricks SQL query to monitor their team’s progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?

They can schedule the query to run every 12 hours from the Jobs UI

They can schedule the query to refresh every 12 hours from the SQL endpoint’s page in Databricks SQL

They can schedule the query to run every 1 day from the Jobs UI

They can schedule the query to refresh every 1 day from the query’s page in Databricks SQL

They can schedule the query to refresh every 1 day from the SQL endpoint’s page in Databricks SQL

Q42. A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?

They could wrap the query using PySpark and use Python’s string variable system to automatically
update the table name

They could request that the data analyst rewrites the query to be run less frequently

They could pass the table into PySpark and develop a robustly tested module on the existing query

They could replace the string-formatted date in the table with a timestamp-formatted date

They could manually replace the date within the table name with the current day’s date

Q43. Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on
separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer
experienced in Databricks suggests there is a better alternative for this type of collaboration.
Which of the following supports the senior data engineer’s claim?

Databricks Notebooks support commenting and notification comments

Databricks Notebooks support the creation of interactive data visualizations

Databricks Notebooks support real-time co-authoring on a single notebook

Databricks Notebooks support the use of multiple languages in the same notebook

Databricks Notebooks support automatic change-tracking and versioning

Q44. A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?

They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query

They need to add the cloud_files(…) wrapper to the JSON file path

They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query

They need to add a live. prefix prior to json. in the FROM line

They need to add a COMMENT line at the beginning of the query

Q45. Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

Data plane

Control plane

Databricks Filesystem

Databricks web application

JDBC data source

Q46. Which of the following SQL keywords can be used to append new rows to an existing Delta table?