[Jan 01, 2023] Latest Databricks-Certified-Professional-Data-Engineer PDF Dumps & Real Tests Free Updated Today [Q33-Q51]

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[Jan 01, 2023] Latest Databricks-Certified-Professional-Data-Engineer PDF Dumps & Real Tests Free Updated Today

Databricks-Certified-Professional-Data-Engineer Dumps With 100% Verified Q&As – Pass Guarantee or Full Refund

NO.33 Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

NO.34 A data engineer is overwriting data in a table by deleting the table and recreating the table. Another data
engineer suggests that this is inefficient and the table should simply be overwritten instead.
Which of the following reasons to overwrite the table instead of deleting and recreating the table is incorrect?

Overwriting a table is an atomic operation and will not leave the table in an unfinished state

Overwriting a table maintains the old version of the table for Time Travel

Overwriting a table is efficient because no files need to be deleted

Overwriting a table results in a clean table history for logging and audit purposes

Overwriting a table allows for concurrent queries to be completed while in progress

NO.35 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

NO.36 A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table(“sales”)
2. .withColumn(“avg_price”, col(“sales”) / col(“units”))
3. .writeStream
4. .option(“checkpointLocation”, checkpointPath)
5. .outputMode(“complete”)
6. ._____
7. .table(“new_sales”)
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?

.processingTime(1)

.processingTime(“once”)

.trigger(processingTime=”once”)

.trigger(once=True)

.trigger(continuous=”once”)

NO.37 A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?

CREATE DATABASE customer360 DELTA LOCATION ‘/customer/customer360’;

CREATE DATABASE customer360 LOCATION ‘/customer/customer360’;

CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION ‘/customer/customer360’;

CREATE DATABASE IF NOT EXISTS customer360;

CREATE DATABASE IF NOT EXISTS customer360 LOCATION ‘/customer/customer360’;

NO.38 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

NO.39 A data engineer wants to create a relational object by pulling data from two tables. The relational object must
be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to
avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

Delta Table

View

Temporary view

Spark SQL Table

Database

NO.40 A denote the event ‘student is female’ and let B denote the event ‘student is French’. In a class of 100 students
suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I
pick a French student, it will be a girl, that is, find P(A|B).

1/3

2/3

1/6

2/6

NO.41 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

NO.42 A data engineer has created a Delta table as part of a data pipeline. Downstream data analysts now need
SELECT permission on the Delta table.
Assuming the data engineer is the Delta table owner, which part of the Databricks Lakehouse Plat-form can
the data engineer use to grant the data analysts the appropriate access?

Jobs
B Dashboards

Data Explorer

Repos

Databricks Filesystem

NO.43 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

NO.44 Which of the following SQL keywords can be used to append new rows to an existing Delta table?

COPY

UNION

INSERT INTO

DELETE

UPDATE

NO.45 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

NO.46 A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the “Refresh” button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?

The queries attached to the dashboard might take a few minutes to run under normal circumstances

The queries attached to the dashboard might all be connected to their own, unstarted Databricks clusters

The Job associated with updating the dashboard might be using a non-pooled endpoint

The SQL endpoint being used by each of the queries might need a few minutes to start up

The queries attached to the dashboard might first be checking to determine if new data is available

NO.47 A junior data engineer has ingested a JSON file into a table raw_table with the following schema:
1. cart_id STRING,
2. items ARRAY<item_id:STRING>
The junior data engineer would like to unnest the items column in raw_table to result in a new table with the
following schema:
1.cart_id STRING,
2.item_id STRING
Which of the following commands should the junior data engineer run to complete this task?

1. SELECT cart_id, flatten(items) AS item_id
2. FROM raw_table;

1. SELECT cart_id, reduce(items) AS item_id
2. FROM raw_table;

1. SELECT cart_id, slice(items) AS item_id
2. FROM raw_table;

1. SELECT cart_id, filter(items) AS item_id
2. FROM raw_table;

1. SELECT cart_id, explode(items) AS item_id
2. FROM raw_table;

NO.48 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

NO.49 Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?

P(E1,E2,E3)P(E1)/P(E2:E3)

P(E1,E2;E3)/P(E2,E3)

P(E1,E2|E3)P(E2|E3)P(E3)

P(E1,E2|E3)P(E3)

P(E1,E2,E3)P(E2)P(E3)

NO.50 A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = “nyc”, store = “100”, and year = “2021”:
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

“{region}+{store}+_sales_+{year}”

“{region}+{store}+”_sales_”+{year}”

f”{region}+{store}+_sales_+{year}”

f”{region}{store}_sales_{year}”

“{region}{store}_sales_{year}”

NO.51 A data engineer wants to horizontally combine two tables as a part of a query. They want to use a shared
column as a key column, and they only want the query result to contain rows whose value in the key column is
present in both tables.
Which of the following SQL commands can they use to accomplish this task?

LEFT JOIN

INNER JOIN

MERGE

OUTER JOIN

UNION