Professional-Data-Engineer Exam Torrent: Google Certified Professional Data Engineer Exam & Professional-Data-Engineer Practice Test

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Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

Google Certified Professional Data Engineer Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following are examples of hyperparameters? (Select 2 answers.)

A. Biases
B. Number of hidden layers
C. Weights
D. Number of nodes in each hidden layer

Answer: B,D

Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview

NEW QUESTION # 92
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?

A. 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.
B. 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.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 93
Which of these statements about exporting data from BigQuery is false?

A. The only supported export destination is Google Cloud Storage.
B. To export more than 1 GB of data, you need to put a wildcard in the destination filename.
C. Data can only be exported in JSON or Avro format.
D. The only compression option available is GZIP.

Answer: C

Explanation:
Explanation
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
Reference: https://cloud.google.com/bigquery/docs/exporting-data

NEW QUESTION # 94
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
D. Add capacity (memory and disk space) to the database server by the order of 200.

Answer: C

NEW QUESTION # 95
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's dat a. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?

A. Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery. dataViewer role on the dataset.
B. Use Analytics Hub to facilitate data sharing.
C. Create authorized datasets to publish shared data in the subscribing team's project.
D. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

Answer: D

Explanation:
To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here's why:
Compatibility with Hive:
Dataproc is a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on-premises Hadoop environment.
This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.
Cost-Effective Storage:
Storing the ORC files in Cloud Storage is cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.
Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.
Hive Integration:
Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.
This setup enables the use of existing Hive queries and scripts without significant modifications.
Steps to Implement:
Copy ORC Files to Cloud Storage:
Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.
Deploy Dataproc Cluster:
Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.
Configure Hive:
Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.
Provide Access to Data Scientists:
Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.
Reference:
Dataproc Documentation
Hive on Dataproc
Google Cloud Storage Documentation

NEW QUESTION # 96
......

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Professional-Data-Engineer Exam Torrent: Google Certified Professional Data Engineer Exam & Professional-Data-Engineer Practice Test

Professional-Data-Engineer Reliable Exam Labs & New Professional-Data-Engineer Test Guide

Exam Topics

Google Certified Professional Data Engineer Exam Sample Questions (Q91-Q96):

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