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Google Cloud Digital Leader exam is a certification program offered by Google Cloud that is designed to validate a candidate's skills in leading digital transformation initiatives using Google Cloud technologies. Cloud-Digital-Leader exam is intended for business leaders, project managers, and technical decision-makers who are responsible for driving digital transformation initiatives within their organizations.
Google Cloud-Digital-Leader (GCDL) is an industry-recognized certification program that validates your knowledge of digital technology services that are being offered by the Google Cloud platform. Cloud-Digital-Leader Exam is ideal for technology professionals who are looking to advance their careers in the digital arena and want to confirm their knowledge of Google Cloud computing services. It covers a wide range of topics such as cloud infrastructure, data analytics, application development, and security.
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Google Cloud Digital Leader Sample Questions (Q131-Q136):
NEW QUESTION # 131
A cinema company wants to build a model to predict customer visit patterns for the coming year. They have three years of customer visit data across 300 theaters; however, the data has been stored in different formats by different theaters. They must train the ML model. What should they do?
- A. Group different format types and train a different model for each group.
- B. Use the last year of data so there are fewer inconsistencies for the model to handle.
- C. Transform the data into a consistent format.
- D. Choose an ML model type that can process different formats of input data.
Answer: C
Explanation:
The correct answer is B. Transform the data into a consistent format. Here's why:
Context of the Questio n : The cinema company wants to build a machine learning model to predict customer visit patterns using three years of customer visit data stored in different formats. For effective ML model training, the input data must be in a consistent format.
Google Cloud Product Relevance:
To build a robust and accurate ML model, the data used for training needs to be cleaned, pre-processed, and transformed into a consistent format. This process ensures that the model can interpret and learn from the data correctly.
Google Cloud provides services like Dataflow for data transformation and processing, and Dataprep for data cleaning and preparation. These tools help standardize data formats before feeding it into a machine learning model.
Why Not Other Options:
A . Choose an ML model type that can process different formats of input data: Most ML models require data to be in a uniform format; choosing a model that processes multiple formats is not standard practice and can lead to inaccuracies.
C . Use the last year of data so there are fewer inconsistencies for the model to handle: This would limit the amount of data available for training, reducing the model's accuracy and effectiveness.
D . Group different format types and train a different model for each group: Training multiple models for different data formats is inefficient and complex compared to transforming the data into a single consistent format.
Google Cloud Digital Leader Reference:
Refer to Dataflow and Dataprep documentation for details on data transformation and preparation services in Google Cloud.
NEW QUESTION # 132
Your organization wants an economical solution to store data such as files, graphical images, and videos and to access and share them securely.
Which Google Cloud product or service should your organization use?
- A. Cloud SQL
- B. BigQuery
- C. Cloud Storage
- D. Cloud Spanner
Answer: C
Explanation:
- Google Storage is GCP's version of AWS Simple Storage Service (S3) and an S3 bucket would be equivalent to a Google Storage bucket across the two clouds Text Description automatically generated
NEW QUESTION # 133
A Multiple projects within your organization have long-term VM usage. Based on current demand, they are able to project into the future and estimate how many VM hours they will use every year. Going in for a committed use contract seems sensible. How can you configure committed use easily across all the projects?
- A. Review the usage of resources by each project on a daily basis. Enable committed use for the following day based on that number, so that it gives maximum granularity without wastage.
- B. Enable committed use with discount sharing for the projects
- C. Take a report of each project's use in the last year. Enable committed use on a per-project basis based on the previous year's numbers.
- D. Share a Google Sheet and request each project team to send in their estimate. Enable committed use accordingly on a per-project basis.
Answer: B
Explanation:
Enable committed use discounts are applied to the project from which you purchased it. To share the discount across multiple projects linked to your Cloud Billing account, enable committed use discount sharing from the console. When you enable committed use discount sharing, all of your current active committed use dis-counts in all the projects under the same Cloud Billing account, including those you previously purchased and new ones you purchase in the future are shared across your Cloud Billing account. Your sustained use discounts are also pooled and shared across all projects within your Cloud Billing account.
NEW QUESTION # 134
Your organization needs to categorize objects in a large group of static images using machine learning. Which Google Cloud product or service should your organization use?
- A. BigQuery ML
- B. AutoML Tables
- C. AutoML Video Intelligence
- D. Cloud Vision API
Answer: D
Explanation:
Reference: https://cloud.google.com/vision
Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.
Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.
NEW QUESTION # 135
You are working with a user to set up an application in a new VPC behind a firewall and it is no-ticed that the user is concerned about data egress. Therefore, to provide assistance you want to con-figure the fewest open egress ports. Which of the following statement is correct?
- A. Set up a high-priority (1000) rule that blocks all egress and a low-priority (65534) rule that allows only the appropriate ports.
- B. Set up a low-priority (65534) rule that blocks all egress and a high-priority rule (1000) that allows only the appropriate ports.
- C. Set up a high-priority (1000) rule that pairs both ingress and egress ports.
- D. Set up a high-priority (1000) rule to allow the appropriate ports.
Answer: B
Explanation:
Explanation
Implied rules Every VPC network has two implied firewall rules. These rules exist, but are not shown in the Cloud Console:
Implied allow egress rule. An egress rule whose action is allow, destination is 0.0.0.0/0, and priority is the lowest possible (65535) lets any instance send traffic to any destination, except for traffic blocked by Google Cloud. A higher priority firewall rule may restrict outbound access. Internet access is allowed if no other firewall rules deny outbound traffic and if the instance has an external IP address or uses a Cloud NAT instance. For more information, see Internet access requirements.
Reference link- https://cloud.google.com/vpc/docs/firewalls
NEW QUESTION # 136
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