Estudio data 2

51. 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 two.)

• A Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

• B For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. 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 Introduce resource hierarchy to leverage access control policy inheritance.

• E Create distinct groups for various teams, and specify groups in Cloud IAM policies.

52. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

• A Cloud Composer

• B cron

• C Cloud Scheduler

• D Workflow Templates on Cloud Dataproc

53. You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

• A Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

• B Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

• C Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

• D Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

1.ou need to choose a database to store time series CPU and memory usage for millions of

computers. You need to store this data in one-second interval samples. Analysts will be

performing real-time, ad hoc analytics against the database. You want to avoid being

charged for every query executed and ensure that the schema design will allow for future

growth of the dataset. Which database and data model should you choose?

• A Create a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

• A Create a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

• C Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second

• D Create a wide table in Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

2. You work for a large bank that operates in locations throughout North America. You are setting up a data storage system that will handle bank account transactions. You require ACID compliance and the ability to access data with SQL. Which solution is appropriate?

• A Store transaction data in Cloud SQL. Use a federated query BigQuery for analysis

• B Store transaction data in Cloud Spanner. Enable stale reads to reduce latency

• C Store transaction data in BigQuery. Disabled the query cache to ensure consistency.

• D Store transaction in Cloud Spanner. Use locking read-write transactions.

3. You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the Trust No One (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

• A Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

• B Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

• C Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

• D Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once.

4. 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 Datastore

• C Cloud Bigtable

• D BigQuery

5. You are migrating a table to BigQuery and are deciding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID, and the city and state in which the store is located. You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state, city, and individual store. How would you model this table for the best query performance?

• A Top-level cluster by state first, then city, then store ID.

• B Partition by transaction time; cluster by state first, then city, then store ID.

• C Top-level cluster by store ID first, then city, then state.

• D Partition by transaction time; cluster by store ID first, then city, then state

6. Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model. You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days. Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

• A Partition the data by transaction date

• A Partition the data by transaction date

• C Shard the data by customer ID.

• C Shard the data by customer ID.

7. You need to deploy additional dependencies to all nodes 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 Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

• B Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

• C Deploy the Cloud SQL Proxy on the Cloud Dataproc master

• D Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet