Google GCP-DE Real Exam Questions and Answers FREE [Q31-Q55]

NO.31 You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Set the BigQuery dataset to be regiona

In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be regiona

Create a scheduled query to make copies of the data to tables suffixed with the time of the backu

In the event of an emergency, use the backup copy of the table.

Set the BigQuery dataset to be multi-regional

In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be multi-regional

Create a scheduled query to make copies of the data to tables suffixed with the time of the backup

In the event of an emergency, use the backup copy of the table.

NO.32 You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

BigQuery

Cloud Bigtable

Cloud Datastore

Cloud SQL for PostgreSQL

NO.33 If you’re running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Do not use a production instance.

Run your test for at least 10 minutes.

Before you test, run a heavy pre-test for several minutes.

Use at least 300 GB of data.

NO.34 Which of the following is NOT true about Dataflow pipelines?

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Dataflow pipelines can consume data from other Google Cloud services

Dataflow pipelines can be programmed in Java

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

NO.35 Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

The CSV data loaded in BigQuery is not flagged as CSV.

The CSV data has invalid rows that were skipped on import.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

The CSV data has not gone through an ETL phase before loading into BigQuery.

NO.36 The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

application node

conditional node

master node

worker node

NO.37 You are developing a software application using Google’s Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

PCollection

Transform

Pipeline

Sink API

NO.38 Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

Preemptible workers cannot use persistent disk.

Preemptible workers cannot store data.

If a preemptible worker is reclaimed, then a replacement worker must be added manually.

A Dataproc cluster cannot have only preemptible workers.

NO.39 You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

Increase the number of max workers

Use a larger instance type for your Cloud Dataflow workers

Change the zone of your Cloud Dataflow pipeline to run in us-central1

Create a temporary table in Cloud Bigtable that will act as a buffer for new data

Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

Create a temporary table in Cloud Spanner that will act as a buffer for new data

Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

NO.40 Cloud Dataproc is a managed Apache Hadoop and Apache service.

Blaze

Spark

Fire

Ignite

NO.41 You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster’s local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF

Mount the Hive tables locally.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste

Mount the Hive tables locally.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste

Then run the Hadoop utility to copy them do HDF

Mount the Hive tables from HDFS.

Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table

Replicate external Hive tables to the native ones.

Load the ORC files into BigQuer

Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table

Replicate external Hive tables to the native ones.

NO.42 You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Consume the stream of data in Cloud Dataflow using Kafka I

Set a sliding time window of 1 hour every 5 minute

Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka I

Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su

Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl

Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour

If that number falls below 4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su

Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer

Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour

If that number falls below 4000, send an alert.

NO.43 You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

Cloud SQL

Cloud Bigtable

Cloud Spanner

Cloud Datastore

NO.44 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?

Cloud Scheduler

Cloud Dataflow

Cloud Functions

Cloud Composer

NO.45 MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

NO.46 Which of these sources can you not load data into BigQuery from?

File upload

Google Drive

Google Cloud Storage

Google Cloud SQL

NO.47 Which of these is not a supported method of putting data into a partitioned table?

If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format “$YYYYMMDD”.

Create a partitioned table and stream new records to it every day.

Use ORDER BY to put a table’s rows into chronological order and then change the table’s type to “Partitioned”.

NO.48 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.

NO.49 You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

cron

Cloud Composer

Cloud Scheduler

Workflow Templates on Cloud Dataproc

NO.50 You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

Use Cloud SQL for storag

Add secondary indexes to support query patterns.

Use Cloud SQL for storag

Use Cloud Dataflow to transform data to support query patterns.

Use Cloud Spanner for storag

Add secondary indexes to support query patterns.

Use Cloud Spanner for storag

Use Cloud Dataflow to transform data to support query patterns.

NO.51 You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients’ personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP

Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

NO.52 You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

Cloud SQL

BigQuery

Cloud Bigtable

Cloud Datastore

NO.53 You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme

Merge the transformed tables together with a SQL query

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

NO.54 An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

NO.55 Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Trigger based on element size in bytes

Trigger that is a combination of other triggers

Trigger based on element count

Trigger based on time