PROFESSIONAL-DATA-ENGINEER VALID DUMPS BOOK - PASS PROFESSIONAL-DATA-ENGINEER EXAM

Professional-Data-Engineer Valid Dumps Book - Pass Professional-Data-Engineer Exam

Professional-Data-Engineer Valid Dumps Book - Pass Professional-Data-Engineer Exam

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Google Professional-Data-Engineer Certification is a valuable asset for data professionals who are seeking to advance their career in the field of data engineering. It demonstrates that a candidate has the skills and knowledge required to design, build, and maintain data processing systems on Google Cloud Platform, which is a highly sought-after skill in today’s data-driven world.

Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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Google Certified Professional Data Engineer Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
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?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: A

Explanation:
To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice:
* Dual-Region Bucket:
* A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.
* The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.
* Turbo Replication:
* Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.
* This minimizes data loss in case of a regional failure.
* Running Dataproc Cluster:
* Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.
* In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.
Steps to Implement:
* Create a Dual-Region Bucket:
* Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.
* Enable turbo replication to ensure rapid data replication between the regions.
* Deploy Dataproc Cluster:
* Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.
* Set Up Failover Plan:
* Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us- central1 region.
* Ensure that the failover process is well-documented and tested to minimize downtime and data loss.
Reference Links:
* Google Cloud Storage Dual-Region
* Turbo Replication in Google Cloud Storage
* Dataproc Documentation


NEW QUESTION # 161
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

  • A. You expect future mutations to have different features from the mutated samples in the database.
  • B. There are roughly equal occurrences of both normal and mutated samples in the database.
  • C. You already have labels for which samples are mutated and which are normal in the database.
  • D. There are very few occurrences of mutations relative to normal samples.
  • E. You expect future mutations to have similar features to the mutated samples in the database.

Answer: D,E

Explanation:
Explanation
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection


NEW QUESTION # 162
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

  • A. node
  • B. label
  • C. type
  • D. zone

Answer: D

Explanation:
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.
create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.
Reference: https://cloud.google.com/dataproc/docs/tutorials/python-library- example#create_a_new_cloud_dataproc_cluste


NEW QUESTION # 163
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

  • A. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
  • B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
  • C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
  • D. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Answer: C

Explanation:
Explanation
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


NEW QUESTION # 164
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?

  • A. Put the data into Google Cloud Storage.
  • B. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
  • C. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
  • D. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Answer: A

Explanation:
First rule of dataproc is to keep data in GCS.


NEW QUESTION # 165
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