Free Google Cloud Data Engineer Professional-Data-Engineer Exam Practice Test

Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?

• Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
• Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
• Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data toBigQuery.
• Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.

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

• Both batch and streaming
• BigQuery cannot be used as a sink
• Only batch
• Only streaming

You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

• Create a Cloud Dataproc Workflow Template
• Create an initialization action to execute the jobs
• Create a Directed Acyclic Graph in Cloud Composer
• Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

You need to choose a database for a new project that has the following requirements: Fully managedAble to automatically scale up Transactionally consistent Able to scale up to 6 TBAble to be queried using SQLWhich database do you choose?

• Cloud SQL
• Cloud Bigtable
• Cloud Spanner
• Cloud Datastore

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

• Convert all daily log tables into date-partitioned tables
• Convert the sharded tables into a single partitioned table
• Enable query caching so you can cache data from previous months
• Create separate views to cover each month, and query from these views

You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app You have reviewed old chat logs and lagged each conversation for intent based on each customer's stated intention for contacting customer service About 70% of customer requests are simple requests that are solved within 10 intents The remaining 30% of inquiries require much longer, more complicated requests Which intents should you automate first?

• Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests
• Automate the more complicated requests first because those require more of the agents' time
• Automate a blend of the shortest and longest intents to be representative of all intents
• Automate intents in places where common words such as "payment" appear only once so the software isn't confused

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model’, table user_features). How should you create the ML pipeline?

• Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
• Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
• Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

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?

• Use K-means Clustering to detect faces in the pixels.
• Use feature engineering to add features for eyes, noses, and mouths to the input data.
• Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
• Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Your startup has a web application that currently serves customers out of a single region in Asi

• You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?
• Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.
• Use a Cloud SQL for PostgreSQL highly available instance first, and 8»gtable with US. Europe, and Asiareplication alter securing funding
• Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.
• Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.

Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing olddata. The current business requirements are:The old data can be deleted anytimeYou plan to use the visualization layer for current and historical reportingThe old data should be available instantly when accessedThere should not be any charges for data retrieval. What should you do to optimize for cost?

• Create the bucket with the Autoclass storage class feature.
• Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline. and 365 days to archive storage class. Delete old data as needed.
• Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline. and 365 days to archive storage class Delete old data as needed.
• Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearlme. 45 days to coldline. and 60 days to archive storage class Delete old data as needed.