Google Professional Data Engineer Exam Practice Test Instant Access

Question 1

You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

AAssign the compute. networkUser role to the Dataflow service agent.

BAssign the compute.networkUser role to the service account that executes the Dataflow pipeline.

CAssign the dataflow, admin role to the Dataflow service agent.

DAssign the dataflow, admin role to the service account that executes the Dataflow pipeline.

Answer : B

To use a Shared VPC network for a Dataflow pipeline, you need to specify the subnetwork parameter with the full URL of the subnetwork, and grant the service account that executes the pipeline the compute.networkUser role in the host project. This role allows the service account to use the subnetworks in the Shared VPC network. The Dataflow service agent does not need this role, as it only creates and manages the resources for the pipeline, but does not execute it. The dataflow.admin role is not related to the network access, but to the permissions to create and delete Dataflow jobs and resources.Reference:

Specify a network and subnetwork | Cloud Dataflow | Google Cloud

How to config dataflow Pipeline to use a Shared VPC?

Question 2

A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open. After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

ACreate a Memorystore instance with a high availability (HA) configuration

BWrite votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery

CWrite votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes
D Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas

Answer : C

Question 3

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

ASend the data to Google Cloud Datastore and then export to BigQuery.

BSend the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

CSend the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

DExport logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Answer : B

Question 4

You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

AUse BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

BTrain an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

CUse the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

DUse TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

Answer : A

Question 5

You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

AWhen creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery's ML. EVALUATE clause without specifying any transformations on the raw input data.

BWhen creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE

CUse a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.

DPreprocess all data using Dataflow. At prediction time, use BigOuery's ML. EVALUATE clause without specifying any further transformations on the input data.

Answer : A

https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning

Question 6

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 7

Which of the following are feature engineering techniques? (Select 2 answers)

AHidden feature layers

BFeature prioritization

CCrossed feature columns

DBucketization of a continuous feature

Answer : C, D

Selecting and crafting the right set of feature columns is key to learning an effective model.

Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.

Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.

https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model

Google Cloud Certified Professional Data Engineer Exam Practice Test