Free Google GCP-DE Exam Dumps Questions & Answers

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.  Use Cloud Deployment Manager to automate access provision.
• C.  Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
• D.  Create distinct groups for various teams, and specify groups in Cloud IAM policies.
• E.  Introduce resource hierarchy to leverage access control policy inheritance.
• F.  For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces

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

Which of the following is NOT true about Dataflow pipelines?

• A.  Dataflow pipelines can be programmed in Java
• B.  Dataflow pipelines can consume data from other Google Cloud services
• C.  Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
• D.  Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

• A.  Combine highly co-dependent features into one representative feature.
• B.  Eliminate features that are highly correlated to the output labels.
• C.  Remove the features that have null values for more than 50% of the training records.
• D.  Instead of feeding in each feature individually, average their values in batches of 3.

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?

• A.  Use Cloud SQL for storag
• B.  Use Cloud SQL for storag
• C.  Add secondary indexes to support query patterns.
• D.  Use Cloud Dataflow to transform data to support query patterns.
• E.  Use Cloud Dataflow to transform data to support query patterns.
• F.  Use Cloud Spanner for storag
• G.  Use Cloud Spanner for storag
• H.  Add secondary indexes to support query patterns.