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Updated Mar-2024 Test Engine to Practice Databricks-Certified-Professional-Data-Engineer Test Questions [Q48-Q63]

Updated Mar-2024 Test Engine to Practice Databricks-Certified-Professional-Data-Engineer Test Questions

Databricks-Certified-Professional-Data-Engineer Real Exam Questions Test Engine Dumps Training With 84 Questions

Databricks Certified Professional Data Engineer exam is a rigorous and comprehensive assessment of a candidate's skills in designing, building, and maintaining data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer exam covers a wide range of topics, including data storage and retrieval, data processing, data transformation, and data visualization. Candidates are tested on their ability to design and implement scalable and reliable data architectures, as well as their proficiency in troubleshooting and optimizing data pipelines.

Q48. In order to use Unity catalog features, which of the following steps needs to be taken on man-aged/external tables in the Databricks workspace?

Enable unity catalog feature in workspace settings

Migrate/upgrade objects in workspace managed/external tables/view to unity catalog

Upgrade to DBR version 15.0

Copy data from workspace to unity catalog

Upgrade workspace to Unity catalog

Q49. Which of the following is true of Delta Lake and the Lakehouse?

Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

Z-order can only be applied to numeric values stored in Delta Lake tables

Explanation
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3.
However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]

Q50. You are working on a marketing team request to identify customers with the same information between two tables CUSTOMERS_2021 and CUSTOMERS_2020 each table contains 25 columns with the same schema, You are looking to identify rows that match between two tables across all columns, which of the following can be used to perform in SQL

1.SELECT * FROM CUSTOMERS_2021
2. UNION
3.SELECT * FROM CUSTOMERS_2020

1.SELECT * FROM CUSTOMERS_2021
2. UNION ALL
3.SELECT * FROM CUSTOMERS_2020

1.SELECT * FROM CUSTOMERS_2021 C1
2.INNER JOIN CUSTOMERS_2020 C2
3.ON C1.CUSTOMER_ID = C2.CUSTOMER_ID

1.SELECT * FROM CUSTOMERS_2021
2. INTERSECT
3.SELECT * FROM CUSTOMERS_2020

1.SELECT * FROM CUSTOMERS_2021
2.EXCEPT
3.SELECT * FROM CUSTOMERS_2020

Q51. A data engineering team needs to query a Delta table to extract rows that all meet the same condi-tion.
However, the team has noticed that the query is running slowly. The team has already tuned the size of the
data files. Upon investigating, the team has concluded that the rows meeting the condition are sparsely located
throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query?

Tuning the file size

Bin-packing

Data skipping

Write as a Parquet file

Z-Ordering

Q52. The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

When the workspace is being configured, make sure that external cloud object storage has been mounted.

Whenever a table is being created, make sure that the location keyword is used.

Whenever a database is being created, make sure that the location keyword is used

When tables are created, make sure that the external keyword is used in the create table statement.

When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.

Q53. Which of the following commands can be used to query a delta table?

1.%python
2.spark.sql(“select * from table_name”)

1.%sql
2.Select * from table_name

Both A & B
(Correct)

1.%python
2.execute.sql(“select * from table”)

1.%python
2.delta.sql(“select * from table”)

Q54. Where are Interactive notebook results stored in Databricks product architecture?

Data plane

Control plane

Data and Control plane

JDBC data source

Databricks web application

Q55. Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum
likelihood estimate for the proportion of “pink” items in the test set of color balls?

2.4

24 0

.24

.48

4.8

Q56. The data engineering team is using a SQL query to review data completeness every day to monitor the ETL job, and query output is being used in multiple dashboards which of the following ap-proaches can be used to set up a schedule and automate this process?

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

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

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

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

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

Q57. 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

Q58. Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION “dbfs:/mnt/sales/*.csv”

FORMAT CSV
OPTIONS ( “true”,”|”)

USING CSV
TYPE ( “true”,”|”)

USING CSV
OPTIONS ( header =”true”, delimiter = “|”)
(Correct)

FORMAT CSV
FORMAT TYPE ( header =”true”, delimiter = “|”)

FORMAT CSV
TYPE ( header =”true”, delimiter = “|”)

Q59. 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)

Q60. A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in thegeo_lookuptable.
Before executing the code, runningSHOWTABLESon the current database indicates the database contains only two tables:geo_lookupandsales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.

Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

Both commands will fail. No new variables, tables, or views will be created.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

Q61. You noticed that colleague is manually copying the notebook with _bkp to store the previous ver-sions, which of the following feature would you recommend instead.

Databricks notebooks support change tracking and versioning

Databricks notebooks should be copied to a local machine and setup source control locally to version the notebooks

Databricks notebooks can be exported into dbc archive files and stored in data lake

Databricks notebook can be exported as HTML and imported at a later time

Q62. 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”)

Q63. 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

Databricks Certified Professional Data Engineer Certification Exam can be attempted by professionals and students who have experience in data engineering, data management, ETL, and data processing. The preparation for the exam can be done via online training courses such as the Databricks Data Engineering Certification Preparation Course, the online Databricks Documentation, and different study materials such as books and videos from verified training providers.

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