Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 241:

  Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

  Which approach should you take?

  A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

  B. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

  C. Use the NOW () function in BigQuery to record the event's time.

  D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

  Correct Answer: B

• Question 242:

  Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way.

  What should you do?

  A. Export the data into a Google Sheet for virtualization.

  B. Create an additional table with only the necessary columns.

  C. Create a view on the table to present to the virtualization tool.

  D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

  Correct Answer: C

• Question 243:

  Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads.

  What should they do?

  A. Store the common data in BigQuery as partitioned tables.

  B. Store the common data in BigQuery and expose authorized views.

  C. Store the common data encoded as Avro in Google Cloud Storage.

  D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster

  Correct Answer: C

• Question 244:

  Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably.

  Which combination of GCP products should you choose?

  A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

  B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

  C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

  D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

  Correct Answer: A

• Question 245:

  The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?

  A. Workers

  B. Masters, workers, and parameter servers

  C. Workers and parameter servers

  D. Parameter servers

  Correct Answer: C

  The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:

  You must set TrainingInput.masterType to specify the type of machine to use for your master node.

  You may set TrainingInput.workerCount to specify the number of workers to use. You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.

  You can specify the type of machine for the master node, but you can't specify more than one master node.

  Reference: https://cloud.google.com/ml-engine/docs/training- overview#job_configuration_parameters

• Question 246:

  When you design a Google Cloud Bigtable schema it is recommended that you _________.

  A. Avoid schema designs that are based on NoSQL concepts

  B. Create schema designs that are based on a relational database design

  C. Avoid schema designs that require atomicity across rows

  D. Create schema designs that require atomicity across rows

  Correct Answer: C

  All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows. Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

• Question 247:

  Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

  A. You expect to store at least 10 TB of data.

  B. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.

  C. You need to integrate with Google BigQuery.

  D. You will not use the data to back a user-facing or latency-sensitive application.

  Correct Answer: C

  For example, if you plan to store extensive historical data for a large number of remote- sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage--reads would be much more frequent in this case, and reads are much slower with HDD storage. Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd

• Question 248:

  You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud native architecture for this scenario?

  A. Edge TPUs as sensor devices for storing and transmitting the messages.

  B. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.

  C. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.

  D. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

  Correct Answer: C

• Question 249:

  You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west 3 region You want to follow Google-recommended practices What should you do?

  A. Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.

  B. Set the constraints/gcp. resourceLocations organization policy constraint to in:eu- locations.

  C. Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.

  D. Set the constraints/gcp. resourceLocations organization policy constraint to in: europe- west3-locations.

  Correct Answer: D

  To ensure that resources are limited to only the europe-west3 region, you should set the organization policy constraint constraints/gcp.resourceLocations to in:europe-west3- locations. This policy restricts the deployment of resources to the

  specified locations, which in this case is the europe-west3 region. By setting this policy, you enforce location compliance across your Google Cloud resources, aligning with the best practices for data governance and regulatory compliance.

  References:

  Professional Data Engineer Certification uide | Learn - Google Cloud1. Preparing for Google Cloud Certification: Cloud Data Engineer2. Professional Data Engineer Certification | Learn | Google Cloud3.

  3: Professional Data Engineer Certification | Learn | Google Cloud 2: Preparing for Google Cloud Certification: Cloud Data Engineer 1: Professional Data Engineer Certification uide | Learn - Google Cloud

• Question 250:

  You work for an airline and you need to store weather data in a BigQuery table Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary

  data and minimize costs.

  What should you do?

  A. Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.

  B. Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.

  C. Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to 30 days.

  D. Create a BigQuery table with a datetime column for the day the weather data refers to.Run a scheduled query to delete rows with a datetime value older than 30 days.

  Correct Answer: B

  Partitioning a table by ingestion time means that the data is divided into partitions based on the time when the data was loaded into the table. This allows you to delete or archive old data by setting a partition expiration policy. You can specify the number of days to keep the data in each partition, and BigQuery automatically deletes the data when it expires. This way, you can avoid storing unnecessary data and minimize costs.