Google Professional Data Engineer Exam Questions Instant Access

Question 1

If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

A1 continuous and 2 categorical

B3 categorical

C3 continuous

D2 continuous and 1 categorical

Answer : D

The columns can be grouped into two types---categorical and continuous columns:

A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.

A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.

Year of birth and income are continuous columns. Country is a categorical column.

You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.

Question 2

You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

ACloud Scheduler

BCloud Dataflow

CCloud Functions

DCloud Composer

Answer : A

Question 3

By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

AWindows at every 100 MB of data

BSingle, Global Window

CWindows at every 1 minute

DWindows at every 10 minutes

Answer : B

Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections

Question 4

You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run. To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

AConvert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery

BIngest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.

CIngest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQLqueries to transform the data, and then write the transformations to a new table

DUse Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery

Answer : A

Question 5

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.

What should you do?

ACreate groups for your users and give those groups access to the dataset

BIntegrate with a single sign-on (SSO) platform, and pass each user's credentials along with the queryrequest

CCreate a service account and grant dataset access to that account. Use the service account's private key to access the dataset

DCreate a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

Answer : C

Question 6

You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runsvery slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

AAllocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory

BAllocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS

CAllocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up

DAllocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

Answer : A

Question 7

You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

ACreate an authorized view that contains the CMEK to decrypt the data when accessed.

BProvide the partner organization a copy of your CMEKs to decrypt the data.

CCopy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.

DExport the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.

Answer : C

If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing.Reference:

Customer-managed Cloud KMS keys

[Authorized views]

[Analytics Hub overview]

[Creating an Analytics Hub listing]

Google Cloud Certified Professional Data Engineer Exam Questions