Best Way To Study For Databricks Databricks-Certified-Professional-Data-Engineer Exam Brilliant Databricks-Certified-Professional-Data-Engineer Exam Questions PDF [Q22-Q38]

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Best Way To Study For Databricks Databricks-Certified-Professional-Data-Engineer Exam Brilliant Databricks-Certified-Professional-Data-Engineer Exam Questions PDF

Updated Verified Pass Databricks-Certified-Professional-Data-Engineer Exam – Real Questions and Answers

Databricks Certified Professional Data Engineer certification is designed for data engineers who work with the Databricks platform and have a deep understanding of data engineering concepts. Databricks Certified Professional Data Engineer Exam certification exam tests the candidate’s ability to design, build, and maintain data pipelines using Databricks, as well as their knowledge of data modeling, data warehousing, and data governance. Databricks Certified Professional Data Engineer Exam certification is recognized globally and indicates that the candidate has the skills and expertise needed to work with Databricks.

Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for data professionals who want to validate their skills and knowledge in building and deploying data engineering solutions using Databricks. Databricks is a unified data analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data projects. Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics such as data ingestion, data processing, data transformation, and data storage using Databricks.

Q22. You currently working with the marketing team to setup a dashboard for ad campaign analysis, since the team is not sure how often the dashboard should be refreshed they have decided to do a manual refresh on an as needed basis. Which of the following steps can be taken to reduce the overall cost of the compute when the team is not using the compute?
*Please note that Databricks recently change the name of SQL Endpoint to SQL Warehouses.

They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse).

They can decrease the maximum bound of the SQL endpoint(SQL Warehouse) scaling range.

They can decrease the cluster size of the SQL endpoint(SQL Warehouse).

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

They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse) and change the Spot Instance Policy from “Reliability Optimized” to “Cost optimized”

Q23. Operations team is using a centralized data quality monitoring system, a user can publish data quality metrics through a webhook, you were asked to develop a process to send messages using a webhook if there is atleast one duplicate record, which of the following approaches can be taken to integrate an alert with current data quality monitoring system

Use notebook and Jobs to use python to publish DQ metrics

Setup an alert to send an email, use python to parse email, and publish a webhook message

Setup an alert with custom template

Setup an alert with custom Webhook destination

Setup an alert with dynamic template

Q24. 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

Q25. Below table temp_data has one column called raw contains JSON data that records temperature for every four hours in the day for the city of Chicago, you are asked to calculate the maximum temperature that was ever recorded for 12:00 PM hour across all the days. Parse the JSON data and use the necessary array function to calculate the max temp.
Table: temp_date
Column: raw
Datatype: string

Expected output: 58

1.select max(raw.chicago.temp[3]) from temp_data

1.select array_max(raw.chicago[*].temp[3]) from temp_data

1.select array_max(from_json(raw[‘chicago’].temp[3],’array<int>’)) from temp_data

1.select array_max(from_json(raw:chicago[*].temp[3],’array<int>’)) from temp_data

1.select max(from_json(raw:chicago[3].temp[3],’array<int>’)) from temp_data

Q26. Which Python variable contains a list of directories to be searched when trying to locate required modules?

importlib.resource path

,sys.path

os-path

pypi.path

pylib.source

Q27. 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

Q28. Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
(Correct)

All of the data and log are stored in a single parquet file

All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.

All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.

All of the data is stored into one parquet file, log files are broken down into one or many json files.

Q29. Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to
estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total
number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to
estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit
rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash
conclusions.

Naive Bayes

Laplace Smoothing

Logistic Regression

Linear Regression

Q30. 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

Q31. You are currently working on reloading customer_sales tables using the below query
1. INSERT OVERWRITE customer_sales
2. SELECT * FROM customers c
3. INNER JOIN sales_monthly s on s.customer_id = c.customer_id
After you ran the above command, the Marketing team quickly wanted to review the old data that was in the table. How does INSERT OVERWRITE impact the data in the customer_sales table if you want to see the previous version of the data prior to running the above statement?

Overwrites the data in the table, all historical versions of the data, you can not time travel to previous versions

Overwrites the data in the table but preserves all historical versions of the data, you can time travel to previous versions

Overwrites the current version of the data but clears all historical versions of the data, so you can not time travel to previous versions.

Appends the data to the current version, you can time travel to previous versions

By default, overwrites the data and schema, you cannot perform time travel

Q32. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

Task queueing resulting from improper thread pool assignment.

Spill resulting from attached volume storage being too small.

Network latency due to some cluster nodes being in different regions from the source data

Skew caused by more data being assigned to a subset of spark-partitions.

Credential validation errors while pulling data from an external system.

Q33. Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?

Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard

The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 AM.

Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.

A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.

Use Incremental refresh to run at 8 AM every day.

Q34. What type of table is created when you create delta table with below command?
CREATE TABLE transactions USING DELTA LOCATION “DBFS:/mnt/bronze/transactions”

Managed delta table

External table

Managed table

Temp table

Delta Lake table

Q35. What are the advantages of the Hashing Features?

Requires the less memory

Less pass through the training data

Easily reverse engineer vectors to determine which original feature mapped to a vector location

Q36. A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

The tables should be updated before the next query is run

The tables should be converted to the Delta format

The tables should be refreshed in the writing cluster before the next query is run

The tables should be altered to include metadata to not cache

The tables should be stored in a cloud-based external system

Q37. You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year’s worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?

Linear regression

Logistic regression

Decision trees

TF-IDF

Q38. 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’

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Best Way To Study For Databricks Databricks-Certified-Professional-Data-Engineer Exam Brilliant Databricks-Certified-Professional-Data-Engineer Exam Questions PDF [Q22-Q38]

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