Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 31:

  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

  A. application node

  B. conditional node

  C. master node

  D. worker node

  Correct Answer: C

  The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix--for example, if your cluster is named "my- cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces

• Question 32:

  You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

  A. Both batch and streaming

  B. BigQuery cannot be used as a sink

  C. Only batch

  D. Only streaming

  Correct Answer: A

  When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io

• Question 33:

  What are the minimum permissions needed for a service account used with Google Dataproc?

  A. Execute to Google Cloud Storage; write to Google Cloud Logging

  B. Write to Google Cloud Storage; read to Google Cloud Logging

  C. Execute to Google Cloud Storage; execute to Google Cloud Logging

  D. Read and write to Google Cloud Storage; write to Google Cloud Logging

  Correct Answer: D

  Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging. Reference: https:// cloud.google.com/dataproc/docs/concepts/service- accounts#important_notes

• Question 34:

  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?

  A. 1 continuous and 2 categorical

  B. 3 categorical

  C. 3 continuous

  D. 2 continuous and 1 categorical

  Correct 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. Reference: https://www.tensorflow.org/tutorials/wide#reading_the_census_data

• Question 35:

  What are two of the characteristics of using online prediction rather than batch prediction?

  A. It is optimized to handle a high volume of data instances in a job and to run more complex models.

  B. Predictions are returned in the response message.

  C. Predictions are written to output files in a Cloud Storage location that you specify.

  D. It is optimized to minimize the latency of serving predictions.

  Correct Answer: BD

  Online prediction Optimized to minimize the latency of serving predictions. Predictions returned in the response message. Batch prediction Optimized to handle a high volume of instances in a job and to run more complex models. Predictions written to output files in a Cloud Storage location that you specify. Reference: https://cloud.google.com/ml-engine/docs/predictionoverview#online_prediction_versus_batch_prediction

• Question 36:

  What is the HBase Shell for Cloud Bigtable?

  A. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

  B. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

  C. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.

  D. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.

  Correct Answer: B

  The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable. Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell

• Question 37:

  All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

  A. before

  B. after

  C. only if

  D. once

  Correct Answer: A

  In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node. The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster. When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster. Reference: https://cloud.google.com/bigtable/docs/overview

• Question 38:

  Why do you need to split a machine learning dataset into training data and test data?

  A. So you can try two different sets of features

  B. To make sure your model is generalized for more than just the training data

  C. To allow you to create unit tests in your code

  D. So you can use one dataset for a wide model and one for a deep model

  Correct Answer: B

  The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on

  an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

  Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

• Question 39:

  Which of the following is not possible using primitive roles?

  A. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.

  B. Give UserA owner access and UserB editor access for all datasets in a project.

  C. Give a user access to view all datasets in a project, but not run queries on them.

  D. Give GroupA owner access and GroupB editor access for all datasets in a project.

  Correct Answer: C

  Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions. Reference: https://cloud.google.com/bigquery/docs/accesscontrol#primitive_iam_roles

• Question 40:

  In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

  A. VPN connection

  B. Special browser

  C. SSH tunnel

  D. FTP connection

  Correct Answer: C

  To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node. Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#connecting_to_the_web_interfaces