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Question 82

Case study -

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.

To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study -

To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an

All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Company Overview -

HipLocal is a community application designed to facilitate communication between people in close proximity. It is used for event planning and organizing sporting events, and for businesses to connect with their local communities. HipLocal launched recently in a few neighborhoods in Dallas and is rapidly growing into a global phenomenon. Its unique style of hyper-local community communication and business outreach is in demand around the world.

Executive Statement -

We are the number one local community app; it's time to take our local community services global. Our venture capital investors want to see rapid growth and the same great experience for new local and virtual communities that come online, whether their members are 10 or 10000 miles away from each other.

Solution Concept -

HipLocal wants to expand their existing service, with updated functionality, in new regions to better serve their global customers. They want to hire and train a new team to support these regions in their time zones. They will need to ensure that the application scales smoothly and provides clear uptime data.

Existing Technical Environment -

HipLocal's environment is a mix of on-premises hardware and infrastructure running in Google Cloud Platform. The HipLocal team understands their application well, but has limited experience in global scale applications. Their existing technical environment is as follows:

* Existing APIs run on Compute Engine virtual machine instances hosted in GCP.

* State is stored in a single instance MySQL database in GCP.

* Data is exported to an on-premises Teradata/Vertica data warehouse.

* Data analytics is performed in an on-premises Hadoop environment.

* The application has no logging.

* There are basic indicators of uptime; alerts are frequently fired when the APIs are unresponsive.

Business Requirements -

HipLocal's investors want to expand their footprint and support the increase in demand they are seeing. Their requirements are:

* Expand availability of the application to new regions.

* Increase the number of concurrent users that can be supported.

* Ensure a consistent experience for users when they travel to different regions.

* Obtain user activity metrics to better understand how to monetize their product.

* Ensure compliance with regulations in the new regions (for example, GDPR).

* Reduce infrastructure management time and cost.

* Adopt the Google-recommended practices for cloud computing.

Technical Requirements -

* The application and backend must provide usage metrics and monitoring.

* APIs require strong authentication and authorization.

* Logging must be increased, and data should be stored in a cloud analytics platform.

* Move to serverless architecture to facilitate elastic scaling.

* Provide authorized access to internal apps in a secure manner.

HipLocal's data science team wants to analyze user reviews.

How should they prepare the data?

    Correct Answer: B

    To prepare user review data for analysis while ensuring privacy and compliance with regulations, it is essential to de-identify any sensitive information contained within the reviews. The Cloud Data Loss Prevention (DLP) API is designed specifically for identifying and transforming sensitive data, including de-identification through methods such as redaction, masking, and tokenization. This helps maintain user privacy by removing or obscuring personal information, making the data safe for analysis without exposing personal details.

Discussion
MickeyRourkeOption: B

Answer is B . Data loss prevention api is used for de-identification not natural language api

celia20200410Option: B

B: https://cloud.google.com/architecture/de-identification-re-identification-pii-using-cloud-dlp De-identification of PII in large-scale datasets using Cloud DLP Cloud DLP enables transformations such as redaction, masking, tokenization, bucketing, and other methods of de-identification.

syu31svcOption: C

I would take C; Cloud Natural Language Processing API for redaction Data Loss Prevention or DLP is not meant for analytics so A and B are wrong while de-identification is for DLP

thewalkerOption: B

The best answer here is B. Use the Cloud Data Loss Prevention API for de-identification of the review dataset. Here's why: Data Loss Prevention API: This API is specifically designed for identifying and protecting sensitive data. It can be used to de-identify data, which means replacing sensitive information with non-sensitive substitutes. This is ideal for user reviews, as they might contain personal information like names, addresses, or other details that need to be protected. De-identification: De-identification is the process of removing or replacing sensitive information in a dataset while preserving its usefulness for analysis. This is crucial for HipLocal's data science team, as they need to analyze user reviews without compromising user privacy.

thewalker

Why other options are less suitable: A. Cloud Data Loss Prevention API for redaction: Redaction involves completely removing sensitive information from a dataset. While this can be effective, it might not be the best approach for user reviews, as it could remove valuable context and insights. C. Cloud Natural Language Processing API for redaction: The Natural Language Processing API is designed for understanding and analyzing text. It's not specifically designed for data protection or de-identification. D. Cloud Natural Language Processing API for de-identification: The Natural Language Processing API is not equipped for de-identification. It's primarily focused on tasks like sentiment analysis, entity recognition, and text summarization.

santoshchauhanOption: B

B. Use the Cloud Data Loss Prevention API for de-identification of the review dataset. For analyzing user reviews, especially if they contain sensitive user information, it's important to protect user privacy. The Cloud Data Loss Prevention (DLP) API provides ways to de-identify sensitive data, which includes redaction, masking, tokenization, and other transformation techniques to obscure or remove sensitive information. De-identification refers to the process of removing or altering information that could be used to identify an individual, making the data safe for analysis without exposing personal information. This is crucial when handling user data to ensure compliance with privacy regulations and maintain user trust.

theseawillclaimOption: B

Of course it's DLP. NLP API makes no sense here.

KadhemOption: B

Answer is B https://cloud.google.com/dlp/docs/deidentify-sensitive-data

wanrltwOption: D

https://www.exam-answer.com/hiplocal-data-preparation "De-identification is the process of removing or obfuscating personally identifiable information (PII) from a dataset, so that individuals cannot be identified. In this case, the data science team needs to analyze user reviews, which could potentially contain PII such as names, email addresses, or other personal information. To protect the privacy of the users, the data should be de-identified before it is analyzed. The Cloud Natural Language Processing API provides various features such as entity recognition, sentiment analysis, and syntax analysis. The API also includes a feature for de-identification, which can be used to remove PII from text data. This feature uses machine learning models to identify and mask or replace PII in the text. In contrast, the Cloud Data Loss Prevention API is designed to identify and redact sensitive data, such as credit card numbers, social security numbers, or other types of PII. It is not intended for general de-identification of text data."

RajanOption: D

D is correct.

jason0001Option: D

The Cloud Natural Language Processing API can help to extract insights from the user reviews, such as sentiment analysis and entity recognition. Additionally, de-identification can help to protect user privacy by removing any personal information from the review data.

tomato123Option: B

B is correct

[Removed]Option: B

It looks like 'redaction' is a type of 'de-identification'. https://cloud.google.com/dlp/docs/transformations-reference#redaction

[Removed]

B. I suspect this is more an English problem than a cloud problem. "redaction of the review dataset" means removing the review itself. "de-identification of the review dataset" means you keep the review text itself, but mask the reviewer's identity so that we do not know any more who wrote it.

p4Option: B

A or B? what speaks for de-identification over reduction? reduction: replace sensitive data with a mask de-identification: replace sensitive data, while keeping possibility of re-identification by trusted party reduction protects user's data even more, whereas de-identification might be better for analyzing the data and link them together, right?

ParagSanyashivOption: B

B is the correct answer

GiniOption: C

I would take C as the purpose is to "analyze user reviews". Generally there is not sensitive data in reviews so I eliminate A and B. Natural Language Processing API is for analyzing things like reviews and comments, it has nothing to do with de-identification.

Gini

Reviewing this question again, the question asks "how to prepare the data" so I change my mind to B, to de-identify the data by Cloud Data Loss Prevention first. After that Natural Language Processing can be used to analyze the data.

GoatSackOption: B

Answer B: https://cloud.google.com/dlp/docs/deidentify-sensitive-data

GoatSack

Backs up: "Ensure compliance with regulations in the new regions (for example, GDPR)."

fralocaOption: C

For me the solution is C