Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 161:

  You are migrating your data warehouse to Google Cloud and decommissioning your on- premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

  A. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real- time updates

  B. BigQuery Data Transfer Service for the migration, Pub/Sub and Dataproc for the real- time updates

  C. gsutil for the migration; Pub/Sub and Dataflow for the real-time updates

  D. gsutil for both the migration and the real-time updates

  Correct Answer: A

• Question 162:

  Your company currently runs a large on-premises cluster using Spark Hive and Hadoop Distributed File System (HDFS) in a colocation facility. The duster is designed to support peak usage on the system, however, many jobs are batch n nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on- premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more servers offerings m order to take advantage of the cloud Because of the tuning of their contract renewal with the colocation facility they have only 2 months for their initial migration How should you recommend they approach thee upcoming migration strategy so they can maximize their cost savings in the cloud will still executing the migration in time?

  A. Migrate the workloads to Dataproc plus HOPS, modernize later

  B. Migrate the workloads to Dataproc plus Cloud Storage modernize later

  C. Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery

  D. Modernize the Spark workload for Dataflow and the Hive workload for BigQuery

  Correct Answer: D

• Question 163:

  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?

  A. Use Transfer Appliance to copy the data to Cloud Storage

  B. Use gsutil cp to compress the content being uploaded to Cloud Storage

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

  D. 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

  Correct Answer: A

• Question 164:

  You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

  A. Get more training examples

  B. Reduce the number of training examples

  C. Use a smaller set of features

  D. Use a larger set of features

  E. Increase the regularization parameters

  F. Decrease the regularization parameters

  Correct Answer: ADF

• Question 165:

  You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

  A. Exceptions in worker code

  B. Job validation

  C. Graph or pipeline construction

  D. Insufficient permissions

  Correct Answer: A

  https://cloud.google.com/dataflow/docs/guides/troubleshooting-your- pipeline#detect_an_exception_in_worker_code While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface.

• Question 166:

  Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

  A. Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.

  B. Add firewall rules in project A so only traffic from the VPC in project A is permitted.

  C. Configure VPC Service Controls in the organization with a perimeter around project A.

  D. Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project.

  Correct Answer: D

  Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic. Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. References: Access control with IAM | Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAM Documentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].

• Question 167:

  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?

  A. 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.

  B. 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.

  C. 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.

  D. 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.

  Correct Answer: C

• Question 168:

  You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through Al models What should you do?

  A. Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

  B. Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets

  C. Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink

  D. Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery

  Correct Answer: A

• Question 169:

  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?

  A. Use Cloud ML Engine for training existing Spark ML models

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

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

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

  Correct Answer: C

  https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml

• Question 170:

  You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.

  How should you securely run this workload?

  A. Restrict the Google Cloud Storage bucket so only you can see the files

  B. Grant the Project Owner role to a service account, and run the job with it

  C. Use a service account with the ability to read the batch files and to write to BigQuery

  D. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

  Correct Answer: B