You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

Use Cloud Vision AutoML with the existing dataset.

Use Cloud Vision AutoML, but reduce your dataset twice.

Use Cloud Vision API by providing custom labels as recognition hints.

Train your own image recognition model leveraging transfer learning techniques.

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?

Use Cloud TPUs after implementing GPU kernel support for your customs ops.

Use Cloud TPUs without any additional adjustment to your code.

Use Cloud TPUs after implementing GPU kernel support for your customs ops.

Use Cloud GPUs after implementing GPU kernel support for your customs ops.

Stay on CPUs, and increase the size of the cluster you're training your model on.

You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio).After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Increase the share of the test sample in the train-test split.

Try to collect more data and increase the size of your dataset.

Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.

Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error.What should you do?

Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.

Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.

Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.

Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

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.

Use Cloud Deployment Manager to automate access provision.

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

Use Cloud Deployment Manager to automate access provision.

Introduce resource hierarchy to leverage access control policy inheritance.

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

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

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.

Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:Single global endpointANSI SQL supportConsistent access to the most up-to-date dataWhat should you do?

Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

Implement BigQuery with no region selected for storage or processing.

Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.

Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.

Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.

Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.

Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:Real-time event streamANSI SQL access to real-time stream and historical dataBatch historical exportsWhich solution should you use?

Cloud Dataflow, Cloud SQL, Cloud Spanner

Cloud Pub/Sub, Cloud Storage, BigQuery

Cloud Dataproc, Cloud Dataflow, BigQuery

Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:Decoupling producer from consumerSpace and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

Increase the number of max workers

Use a larger instance type for your Cloud Dataflow workers

Increase the number of max workers

Use a larger instance type for your Cloud Dataflow workers

Change the zone of your Cloud Dataflow pipeline to run in us-central1

Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

Increase the number of nodes in the Cloud Bigtable cluster

Configure your Cloud Dataflow pipeline to use local execution

Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

Increase the number of nodes in the Cloud Bigtable cluster

Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

Create a Directed Acyclic Graph in Cloud Composer

Create a Cloud Dataproc Workflow Template

Create an initialization action to execute the jobs

Create a Directed Acyclic Graph in Cloud Composer

Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

Create an API using App Engine to receive and send messages to the applications

Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Create a table on Cloud SQL, and insert and delete rows with the job information

Create a table on Cloud Spanner, and insert and delete rows with the job information

You need to create a new transaction table in Cloud Spanner that stores product sales dat a. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A random universally unique identifier number (version 4 UUID)

A concatenation of the product name and the current epoch time

A random universally unique identifier number (version 4 UUID)

The original order identification number from the sales system, which is a monotonically increasing integer

Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.

Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.

Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.

Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.

Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update.What should you do?

Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name

Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name

Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

Use Transfer Appliance to copy the data to Cloud Storage

Use gsutil cp ñJ to compress the content being uploaded to Cloud Storage

Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:Executing the transformations on a scheduleEnabling non-developer analysts to modify transformationsProviding a graphical tool for designing transformationsWhat should you do?

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

These primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed FileSystem (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.

Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables.Replicate external Hive tables to the native ones.

Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.

You are working on a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component in order to train and serve the model your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables.What should you do?

Cloud Data Fusion to assign each city to a region that is labeled as 1, 2 3, 4, or 5, and then use that number to represent the city in the model.

Use SQL in BigQuery to transform the stale column using a one-hot encoding method, and make each city a column with binary values.

Create a new view with BigQuery that does not include a column which city information.

Cloud Data Fusion to assign each city to a region that is labeled as 1, 2 3, 4, or 5, and then use that number to represent the city in the model.

Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file and upload that as part of your model to BigQuery ML.

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?

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

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

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

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.

You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the dat a. They should only see certain tables based on their team membership. How should you set user permissions?

Assign the users/groups data viewer access at the table level for each table

Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views

Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views

Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside

You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary dat a. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory

Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS

Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up

Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

You work for an advertising company, and you've developed a Spark ML model to predict click- through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

Use Cloud ML Engine for training existing Spark ML models

Rewrite your models on TensorFlow, and start using Cloud ML Engine

Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.

Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.

Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for longrunning batch jobs. You want to use a managed service. What should you do?

Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers.Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances.Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://

Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances. Store data in HDFS. Change references in scripts from hdfs:// to gs://

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

Train a classifier with deep neural networks, because neural networks would always beat SVMs

Deploy the model and measure the real-world AUC; it's always higher because of generalization

Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

You need to deploy additional dependencies to all 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?

Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Deploy the Cloud SQL Proxy on the Cloud Dataproc master

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

Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

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

You 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?

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

Create a table in BigQuery, and append the new samples for CPU and memory to the table

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

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

Create a wide table in Cloud 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.

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 dat a. What should you do?

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 and rotate the key once.

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.

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 and rotate the key once.

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.

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.

You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver

Export the information to Cloud Stackdriver, and set up an Alerting policy

Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver

Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs

Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

Google Professional Data Engineer Practice Test 6 - Free Online Exam Questions 201-240

You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

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Google Professional Data Engineer Practice Test 6

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Google Professional Data Engineer Practice Tests

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