Google Professional Data Engineer Exam Practice Test Instant Access

Question 1

When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.

AHTTPS

BVPN

CSOCKS

DHTTP

Answer : C

When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.

Question 2

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

AUse K-means Clustering to detect faces in the pixels.

BUse feature engineering to add features for eyes, noses, and mouths to the input data.

CUse deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

DBuild a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer : C

Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as ''deep'' learning. So deep is a strictly defined, technical term that means more than one hidden layer.

In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the

previous layer.

A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to 'build' these features using previous hidden layers that detect low-level features, such as lines.

Feature engineering is difficult to perform on raw image data.

K-means Clustering is an unsupervised learning method used to categorize unlabeled data.

Question 3

You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

ACall the Cloud Natural Language API from your application. Process the generated Entity Analysis as
labels.

BCall the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

CBuild and train a text classification model using TensorFlow. Deploy the model using Cloud Machine
Learning Engine. Call the model from your application and process the results as labels.

DBuild and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

Answer : B

Question 4

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

ATransform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

BTransform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery
permanent linked tables for query.

CCompress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

DCompress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into
Cloud Bigtable for query.

Answer : D

Question 5

Which of these sources can you not load data into BigQuery from?

AFile upload

BGoogle Drive

CGoogle Cloud Storage

DGoogle Cloud SQL

Answer : D

You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.

Question 6

You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?

AUse Compute Engi.no startup scriots to pull container Images, and use gloud commands to provision the infrastructure.

BUse GKE to autoscale containers, and use gloud commands to provision the infrastructure.

CUse Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.

DUse Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.

Answer : C

https://cloud.google.com/architecture/managing-infrastructure-as-code

Question 7

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log dat

a. How should you set up the log data transfer into Google Cloud?

AUse Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

BUse Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

CUse BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

DUse BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
storage bucket as a final destination.

Answer : B

Google Cloud Certified Professional Data Engineer Exam Practice Test