Google Professional-Data-Engineer Test Engine Dumps Training With 270 Questions
Professional-Data-Engineer Questions Pass on Your First Attempt Dumps for Google Cloud Certified Certified
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.
What is the duration, language, and format of Google Professional Data Engineer Exam
- Language: English (U.S.), Japanese, Spanish, and Portuguese
- Length of Examination: 120 minutes
- Format: Multiple choices, multiple answers
- Number of Questions: 50-60
- Cost: $200
NEW QUESTION 110
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.
- A. Fire
- B. Ignite
- C. Spark
- D. Blaze
Answer: C
Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
NEW QUESTION 111
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 data. What should you do?
- A. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
- B. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
- C. Use gcloud kms keys createto create a symmetric key. Then use gcloud kms encryptto 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.
- D. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key. Use gsutil cpto upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once.
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION 112
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world
manage their resources and transport them to their final destination. The company has grown rapidly,
expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has
become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking
shipments in real time at the parcel level. However, they are unable to deploy it because their technology
stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to
further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of
their loads
Perform analytics on all their orders and shipment logs, which contain both structured and unstructured
data, to determine how best to deploy resources, which markets to expand info. They also want to use
predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments
Accurately track every shipment worldwide using proprietary technology
Improve business agility and speed of innovation through rapid provisioning of new resources
Analyze and optimize architecture for performance in the cloud
Migrate fully to the cloud if all other requirements are met
Technical Requirements
Handle both streaming and batch data
Migrate existing Hadoop workloads
Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth
and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving
data around.
We need to organize our information so we can more easily understand where our customers are and
what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our
technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I
cannot get them to do the things that really matter, such as organizing our data, building the analytics, and
figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing
where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
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. Create identity and access management (IAM) roles on the appropriate columns, so only they appear
in a query. - B. Export the data into a Google Sheet for virtualization.
- C. Create a view on the table to present to the virtualization tool.
- D. Create an additional table with only the necessary columns.
Answer: C
NEW QUESTION 113
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat
a. Which three machine learning applications can you use? (Choose three.)
- A. Reinforcement learning to predict the location of a transaction.
- B. Supervised learning to determine which transactions are most likely to be fraudulent.
- C. Unsupervised learning to predict the location of a transaction.
- D. Clustering to divide the transactions into N categories based on feature similarity.
- E. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- F. Supervised learning to predict the location of a transaction.
Answer: D,E,F
NEW QUESTION 114
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
- A. A good use for the wide and deep model is a recommender system.
- B. The wide model is used for generalization, while the deep model is used for memorization.
- C. The wide model is used for memorization, while the deep model is used for generalization.
- D. A good use for the wide and deep model is a small-scale linear regression problem.
Answer: A,C
Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
NEW QUESTION 115
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.
- A. HTTPS
- B. HTTP
- C. VPN
- D. SOCKS
Answer: D
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces
NEW QUESTION 116
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. Only Batch Data Pipelines
- B. Only Streaming Data Pipelines
- C. Both Batch and Streaming Data Pipelines
- D. None of the above
Answer: C
Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines
NEW QUESTION 117
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. Use a service account with the ability to read the batch files and to write to BigQuery
- B. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
- C. Grant the Project Owner role to a service account, and run the job with it
- D. Restrict the Google Cloud Storage bucket so only you can see the files
Answer: A
NEW QUESTION 118
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. Triggers
- B. Windows
- C. OutputCriteria
- D. Sessions
Answer: A
Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/ transforms/windowing/Trigger
NEW QUESTION 119
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
Answer: C
Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION 120
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?
- A. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
- B. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
- C. Convert your batch BQ queries into interactive BQ queries.
- D. Create an additional project to overcome the 2K on-demand per-project quota.
Answer: A
NEW QUESTION 121
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
- A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
- B. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application. - C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
- D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
Answer: C
NEW QUESTION 122
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
- A. HDFS with Hive
- B. MongoDB
- C. MySQL
- D. Redis
- E. HBase
- F. Cassandra
Answer: A,B,E
Explanation:
Explanation/Reference:
NEW QUESTION 123
Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property=_____.
- A. null
- B. details
- C. id
- D. value
Answer: D
Explanation:
To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated. The formatting is as follows: file_prefix:property=value.
NEW QUESTION 124
Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers
- A. Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.
- B. Insert each new CDC record and corresponding operation type into a staging table in real time
- C. Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.
- D. Periodically DELETE outdated records from the reporting table
Periodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table
Answer: A,D
NEW QUESTION 125
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?
- A. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
- B. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
- C. Convert your batch BQ queries into interactive BQ queries.
- D. Create an additional project to overcome the 2K on-demand per-project quota.
Answer: A
Explanation:
Explanation
Explanation/Reference:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery
NEW QUESTION 126
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?
- A. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
- B. Increase the cluster size with more non-preemptible workers.
- C. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
- D. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
Answer: C
NEW QUESTION 127
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. Cloud Dataflow
- B. Cloud Bigtable
- C. Cloud Datastore
- D. BigQuery
Answer: D
Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
NEW QUESTION 128
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Certification Path
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
Professional-Data-Engineer Practice Test Pdf Exam Material: https://www.itpassleader.com/Google/Professional-Data-Engineer-dumps-pass-exam.html
Professional-Data-Engineer Answers Professional-Data-Engineer Free Demo Are Based On The Real Exam: https://drive.google.com/open?id=1oN8fqdZbPhE00XNQivwANcgWkE6n8orn