You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?

1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions. 2. Enable turbo replication. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region. 4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.

1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions. 2. Enable turbo replication. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region. 4. In case of a regional failure, redeploy your Dataproc duster to the us-south1 region and continue reading from the same bucket.

1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions. 2. Enable turbo replication. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region. 4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.

1. Create a Cloud Storage bucket in the US multi-region. 2. Run the Dataproc cluster in a zone in the ua-central1 region, reading data from the US multi-region bucket. 3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.

1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region. 2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket. 3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region. 4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.

Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.

Create a BigQuery scheduled query to replicate all customer data into team projects.

Enable each team to create materialized views of the data they need to access in their projects.

Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.

Ask each team to create authorized views of their data. Grant the biquery. jobUser role to each team.

You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuory. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?

Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.

Ensure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.

Ensure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.

Create a VPC Service Controls perimeter that contains the VPC network and add Dataflow. Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.

Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.

You currently use a SQL-based tool to visualize your data stored in BigQuery The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically.Reference: the materialized views in the data visualization tool.

Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_3taleness parameter to 4 hours and the enable_refresh parameter to true.Reference: the materialized views in the data visualization tool.

Create views for the visualization queries.Reference: the views in the data visualization tool.

Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically.Reference: the materialized views in the data visualization tool.

Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours.Reference: the parquet files in the data visualization tool.

Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a table size of 10 PB. You need to create a dashboard for the support team to view the order history. The dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results. However, the dashboard is slow to show the results when applying the filters to the following query: How should you redesign the BigQuery table to support faster access?

Cluster the table by country and username fields

Cluster the table by country field, and partition by username field.

Partition the table by country and username fields.

Cluster the table by country and username fields

Partition the table by _PARTITIONTIME.

You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?

Leave the Authorized Network empty. Use Cloud SQL Auth proxy on all applications.

Add all application networks to Authorized Network and regularly update them.

Add CIDR 0.0.0.0/0 network to Authorized Network. Use Identity and Access Management (1AM) to add users.

Leave the Authorized Network empty. Use Cloud SQL Auth proxy on all applications.

Add CIDR 0.0.0.0/0 network to Authorized Network. Use Cloud SOL Auth proxy on all applications.

You are creating the CI'CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

1 Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.

1. Use Cloud Build to build a container and the Kubemetes Pod Operator to deploy the code of the DAG to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.

1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

1 Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.

1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.

You have two projects where you run BigQuery jobs:* One project runs production jobs that have strict completion time SLAs. These are high priority jobs that must have the required compute resources available when needed. These jobs generally never go below a 300 slot utilization, but occasionally spike up an additional 500 slots.* The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity.You need to ensure that both projects have the appropriate compute resources available. What should you do?

Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing.

Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots.

Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing.

Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignore_idle_slot3 flag to False.

Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots.

You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?

Build materialized views on top of the sales table to aggregate data at the day and month level.

Build authorized views on top of the sales table to aggregate data at the day and month level.

Enable Bl Engine and add your sales table as a preferred table.

Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.

You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production data. What should you do?

Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.

Create a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-data-loss data protection mode.

Initiate a manual tailover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in the production environment.

Increase one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss data protection mode.

Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.

You are planning to use Cloud Storage as pad of your data lake solution. The Cloud Storage bucket will contain objects ingested from external systems. Each object will be ingested once, and the access patterns of individual objects will be random. You want to minimize the cost of storing and retrieving these objects. You want to ensure that any cost optimization efforts are transparent to the users and applications. What should you do?

Create a Cloud Storage bucket with Autoclass enabled.

Create a Cloud Storage bucket with an Object Lifecycle Management policy to transition objects from Standard to Coldline storage class if an object age reaches 30 days.

Create a Cloud Storage bucket with an Object Lifecycle Management policy to transition objects from Standard to Coldline storage class if an object is not live.

Create two Cloud Storage buckets. Use the Standard storage class for the first bucket, and use the Coldline storage class for the second bucket. Migrate objects from the first bucket to the second bucket after 30 days.

You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

1. Store the historical data in BigQuery for analytics. 2. Use a materialized view to precompute the last state of a product. 3. Serve the last state data directly from BigQuery to the API.

1. Store the historical data in BigQuery for analytics. 2. In a Cloud SQL table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the API.

1. Store the historical data in Cloud SQL for analytics. 2. In a separate table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the API.

1. Store the products as a collection in Firestore with each product having a set of historical changes. 2. Use simple and compound queries for analytics. 3. Serve the last state data directly from Firestore to the API.

1. Store the historical data in BigQuery for analytics. 2. Use a materialized view to precompute the last state of a product. 3. Serve the last state data directly from BigQuery to the API.

You are designing a messaging system by using Pub/Sub to process clickstream data with an event-driven consumer app that relies on a push subscription. You need to configure the messaging system that is reliable enough to handle temporary downtime of the consumer app. You also need the messaging system to store the input messages that cannot be consumed by the subscriber. The system needs to retry failed messages gradually, avoiding overloading the consumer app, and store the failed messages after a maximum of 10 retries in a topic. How should you configure the Pub/Sub subscription?

Use exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

Increase the acknowledgement deadline to 10 minutes.

Use immediate redelivery as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

Use exponential backoff as the subscription retry policy, and configure dead lettering to the same source topic with maximum delivery attempts set to 10.

Use exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?

Execute the job in a new Dataproc cluster.

Execute as a Dataproc Serverless job.

Execute the job as part of a deployment in a new Google Kubernetes Engine cluster.

Execute the job from a new Compute Engine VM.

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?

Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.

Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.

Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.

Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.

Google Professional Data Engineer Practice Test 10 - Free Online Exam Questions 361-377

You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

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Google Professional Data Engineer Practice Test 10

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Google Professional Data Engineer Practice Tests

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