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NEW QUESTION # 133
You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

• A. Set a retention policy. Lock the retention policy.
• B. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
• C. Enable the Object Versioning feature. Add a lifecycle rule.
• D. Enable the Object Versioning feature. Create a copy in a bucket in a different region.

Answer: A

Explanation:
To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:
Retention Policy:
A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified.
This ensures data immutability.
Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.
Locking the Retention Policy:
Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.
Steps to Implement:
Set the Retention Policy:
Define a retention period for the bucket to ensure that all objects are protected for the required duration.
Lock the Retention Policy:
Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.
Reference Links:
Cloud Storage Retention Policy Documentation
How to Set a Retention Policy

NEW QUESTION # 134
Cloud Bigtable is Google's ______ Big Data database service.

• A. SQL Server
• B. NoSQL
• C. mySQL
• D. Relational

Answer: B

Explanation:
Explanation
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail.
It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
Reference: https://cloud.google.com/bigtable/

NEW QUESTION # 135
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl.
being able to reprocess all failing data).
What should you do?

• A. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
• B. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
• C. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
• D. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Answer: D

NEW QUESTION # 136
You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?

• A. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.2. Enable turbo replication.3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region.4. In case of a regional failure, redeploy your Dataproc duster to the us-south1 region and continue reading from the same bucket.
• B. 1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region.2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.
• C. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.2. Enable turbo replication.3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.
• D. 1. Create a Cloud Storage bucket in the US multi-region.2. Run the Dataproc cluster in a zone in the ua- central1 region, reading data from the US multi-region bucket.3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.

Answer: C

Explanation:
To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice:
Dual-Region Bucket:
A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.
The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.
Turbo Replication:
Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.
This minimizes data loss in case of a regional failure.
Running Dataproc Cluster:
Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.
In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.
Steps to Implement:
Create a Dual-Region Bucket:
Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.
Enable turbo replication to ensure rapid data replication between the regions.
Deploy Dataproc Cluster:
Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.
Set Up Failover Plan:
Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us-central1 region.
Ensure that the failover process is well-documented and tested to minimize downtime and data loss.
Reference Links:
Google Cloud Storage Dual-Region
Turbo Replication in Google Cloud Storage
Dataproc Documentation

NEW QUESTION # 137
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

• A. Use Cloud TPUs without any additional adjustment to your code.
• B. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
• C. Stay on CPUs, and increase the size of the cluster you're training your model on.
• D. Use Cloud TPUs after implementing GPU kernel support for your customs ops.

Answer: D

NEW QUESTION # 138
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Add ", UNNEST(city)" before the WHERE clause.
• B. Change "person" to "person.city".
• C. Change "person" to "city.person".
• D. Add ", UNNEST(person)" before the WHERE clause.

Answer: D

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results

NEW QUESTION # 139
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub
/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

• A. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
• B. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
• C. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
• D. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: A

NEW QUESTION # 140
You are building a teal-lime prediction engine that streams files, which may contain Pll (personal identifiable information) data, into Cloud Storage and eventually into BigQuery You want to ensure that the sensitive data is masked but still maintains referential Integrity, because names and emails are often used as join keys How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the Pll data is not accessible by unauthorized individuals?

• A. Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non-tokenized data in a locked-down button.
• B. Create a pseudonym by replacing Pll data with a cryptographic format-preserving token
• C. Redact all Pll data, and store a version of the unredacted data in a locked-down bucket
• D. Scan every table in BigQuery, and mask the data it finds that has Pll

Answer: A

NEW QUESTION # 141
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 a service account and grant dataset access to that account. Use the service account's private key to access the dataset
• B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
• C. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
• D. Create groups for your users and give those groups access to the dataset

Answer: A

Explanation:
Explanation

NEW QUESTION # 142
You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery In your current relational database, the author information is kept in a separate table and joined to the book information on a common key Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

• A. Create a table that includes information about the books and authors, but nest the author fields inside the author column
• B. Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today
• C. Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc
• D. Keep the schema the same, create a view that joins all of the tables, and always query the view

Answer: A

NEW QUESTION # 143
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the
world. The company has patents for innovative optical communications hardware. Based on these patents,
they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to
overcome communications challenges in space. Fundamental to their operation, they need to create a
distributed data infrastructure that drives real-time analysis and incorporates machine learning to
continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the
network allowing them to account for the impact of dynamic regional politics on location availability and
cost.
Their management and operations teams are situated all around the globe creating many-to-many
relationship between data consumers and provides in their system. After careful consideration, they
decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data

Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows

each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately

100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems

both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive
hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize
our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data
secure. We also need environments in which our data scientists can carefully study and quickly adapt our
models. Because we rely on automation to process our data, we also need our development and test
environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on
automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to
work on our high-value problems instead of problems with our data pipelines.
You need to compose visualizations for operations teams with the following requirements:
The report must include telemetry data from all 50,000 installations for the most resent 6 weeks

(sampling once every minute).
The report must not be more than 3 hours delayed from live data.

The actionable report should only show suboptimal links.

Most suboptimal links should be sorted to the top.

Suboptimal links can be grouped and filtered by regional geography.

User response time to load the report must be <5 seconds.

Which approach meets the requirements?

• A. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to
  your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a
  table.
• B. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries
  all rows, applies a function to derive the metric, and then renders results in a table using the Google
  charts and visualization API.
• C. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show
  only suboptimal links in a table.
• D. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates
  the metric, and shows only suboptimal rows in a table in Google Sheets.

Answer: B

NEW QUESTION # 144
You created a new version of a Dataflow streaming data ingestion pipeline that reads from Pub/Sub and writes to BigQuery. The previous version of the pipeline that runs in production uses a 5-minute window for processing. You need to deploy the new version of the pipeline without losing any data, creating inconsistencies, or increasing the processing latency by more than 10 minutes. What should you do?

• A. Cancel the old pipeline, then start the new pipeline.
• B. Snapshot the old pipeline, stop the old pipeline, and then start the new pipeline from the snapshot.
• C. Update the old pipeline with the new pipeline code.
• D. Drain the old pipeline, then start the new pipeline.

Answer: D

NEW QUESTION # 145
You are using Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in. How should you design your row key and tables to ensure that you can access the data with the simplest query?

• A. For each index, have a separate table and use a reverse timestamp as the row key design
• B. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.
• C. Create one unique table for all of the indices, and then use the index and timestamp as the row key design
• D. For each index, have a separate table and use a timestamp as the row key design

Answer: B

Explanation:
If you usually retrieve the most recent records first, you can use a reversed timestamp in the row key by subtracting the timestamp from your programming language's maximum value for long integers (in Java, java.lang.Long.MAX_VALUE). With a reversed timestamp, the records will be ordered from most recent to least recent.

NEW QUESTION # 146
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall dat
a. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

• A. Create a separate table for each ID.
• B. Use the LIMIT keyword to reduce the number of rows returned.
• C. Recreate the table with a partitioning column and clustering column.
• D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.

Answer: C

NEW QUESTION # 147
Which of the following statements is NOT true regarding Bigtable access roles?

• A. You can configure access control only at the project level.
• B. To give a user access to only one table in a project, you must configure access through your application.
• C. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
• D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

Answer: C

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project. Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances.
Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 148
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of

their loads
Perform analytics on all their orders and shipment logs, which contain both structured and unstructured

data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases

8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs

60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
Storage appliances

- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
Apache Hadoop /Spark servers

- Core Data Lake
- Data analysis workloads
20 miscellaneous servers

- Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.

Aggregate data in a centralized Data Lake for analysis

Use historical data to perform predictive analytics on future shipments

Accurately track every shipment worldwide using proprietary technology

Improve business agility and speed of innovation through rapid provisioning of new resources

Analyze and optimize architecture for performance in the cloud

Migrate fully to the cloud if all other requirements are met

Technical Requirements
Handle both streaming and batch data

Migrate existing Hadoop workloads

Ensure architecture is scalable and elastic to meet the changing demands of the company.

Use managed services whenever possible

Encrypt data flight and at rest

Connect a VPN between the production data center and cloud environment

SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

• A. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
• B. Export the data into a Google Sheet for virtualization.
• C. Create a view on the table to present to the virtualization tool.
• D. Create an additional table with only the necessary columns.

Answer: C

NEW QUESTION # 149
You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
* Each department should have access only to their data.
* Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
* Each department has data analysts who need to be able to query but not modify data.
How should you set access to the data in BigQuery?

• A. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.
• B. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
• C. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.
• D. Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.

Answer: C

NEW QUESTION # 150
Your team has created several BigQuery curated datasets containing anonymized industry benchmark data. You want to make these datasets easily discoverable and accessible for querying by external partner companies within their own Google Cloud projects. You need a secure and scalable solution. What should you do?

• A. Create authorized views for each dataset and grant access to each partner.
• B. Publish the datasets as listings within BigQuery sharing (Analytics Hub).
• C. Export the datasets to partner-specific Cloud Storage buckets.
• D. Grant the roles/bigquery.dataViewer IAM role to the partner group email addresses on the datasets.

Answer: B

Explanation:
Publishing the datasets as listings in BigQuery sharing (Analytics Hub) provides a secure, scalable way to share curated datasets across Google Cloud projects without copying data. It enables partners to discover datasets through a centralized catalog and query them directly from their own projects while you retain full control over access and updates.

NEW QUESTION # 151
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