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NEW QUESTION # 12
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.
- A. only if
- B. after
- C. before
- D. once
Answer: C
Explanation:
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
NEW QUESTION # 13
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Create a table on Cloud SQL, and insert and delete rows with the job information
- B. Create a table on Cloud Spanner, and insert and delete rows with the job information
- C. Create an API using App Engine to receive and send messages to the applications
- D. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
Answer: D
NEW QUESTION # 14
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
- A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- B. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- C. Use Cloud Deployment Manager to automate access provision.
- D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
- F. Introduce resource hierarchy to leverage access control policy inheritance.
Answer: D,F
NEW QUESTION # 15
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?
- A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
- B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
- C. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
- D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
Answer: C
NEW QUESTION # 16
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?
- A. Create groups for your users and give those groups access to the dataset
- B. Use the service account's private key to access the dataset
- C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- D. Create a dummy user and grant dataset access to that use
- E. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
- F. Create a service account and grant dataset access to that accoun
Answer: F
NEW QUESTION # 17
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?
- A. Cloud Bigtable
- B. BigQuery
- C. Cloud Datastore
- D. Cloud SQL
Answer: B
NEW QUESTION # 18
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?
- A. Cloud Bigtable
- B. Cloud Spanner
- C. Cloud SQL
- D. Cloud Datastore
Answer: C
NEW QUESTION # 19
What are the minimum permissions needed for a service account used with Google Dataproc?
- A. Write to Google Cloud Storage; read to Google Cloud Logging
- B. Execute to Google Cloud Storage; write 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
Answer: D
Explanation:
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
NEW QUESTION # 20
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?
- A. BigQuery and Cloud Storage
- B. Cloud Bigtable and Cloud SQL
- C. Cloud Datastore and Cloud Bigtable
- D. BigQuery and Cloud Bigtable
Answer: D
NEW QUESTION # 21
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?
- A. Cloud Spanner
- B. Cloud Bigtable
- C. Cloud Datastore
- D. Cloud SQL
Answer: A
NEW QUESTION # 22
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. SOCKS
- B. HTTPS
- C. VPN
- D. HTTP
Answer: A
Explanation:
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.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION # 23
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?
- A. In the event of an emergency, use the backup copy of the table.
- B. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
- C. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
- D. In the event of an emergency, use a point-in-time snapshot to recover the data.
- E. Set the BigQuery dataset to be regiona
- F. In the event of an emergency, use a point-in-time snapshot to recover the data.
- G. Set the BigQuery dataset to be multi-regional
- H. In the event of an emergency, use the backup copy of the table.
- I. Set the BigQuery dataset to be regiona
- J. Set the BigQuery dataset to be multi-regional
Answer: F
NEW QUESTION # 24
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?
- A. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- B. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
Answer: A
NEW QUESTION # 25
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
- A. Preemptible workers cannot use persistent disk.
- B. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
- C. Preemptible workers cannot store data.
- D. A Dataproc cluster cannot have only preemptible workers.
Answer: C,D
Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms
NEW QUESTION # 26
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?
- A. Cloud SQL for PostgreSQL
- B. Cloud Bigtable
- C. BigQuery
- D. Cloud Datastore
Answer: C
NEW QUESTION # 27
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?
- A. BigQuery
- B. Cloud Bigtable
- C. Cloud SQL
- D. Cloud Datastore
Answer: C
NEW QUESTION # 28
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?
- A. Change references in scripts from hdfs:// to gs://
- B. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
- C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- D. Use a standard persistent disk and 50% preemptible worker
- E. Change references in scripts from hdfs:// to gs://
- F. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
- G. Install the Cloud Storage connector, and store the data in Cloud Storag
- H. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- I. Use an SSD persistent disk and 50% preemptible worker
- J. Deploy a Cloud Dataproc cluste
- K. Deploy a Cloud Dataproc cluste
Answer: J
NEW QUESTION # 29
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Store the data in Google Cloud Datastor
- B. Load the data every 30 minutes into a new partitioned table in BigQuery.
- C. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- D. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- E. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- F. Store the data in a file in a regional Google Cloud Storage bucke
Answer: B
NEW QUESTION # 30
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?
- A. Transform
- B. Sink API
- C. Pipeline
- D. PCollection
Answer: A
Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model
NEW QUESTION # 31
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?
- A. Merge the transformed tables together with a SQL query
- B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
- C. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- D. 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
- E. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
- F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
Answer: B
NEW QUESTION # 32
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