2023 Valid Professional-Data-Engineer Real Exam Questions, practice Google Cloud Certified
Latest Success Metrics For Actual Professional-Data-Engineer Exam (Updated 270 Questions)
To become a Google Certified Professional Data Engineer, candidates must have a strong foundation in data engineering concepts and technologies. They must also possess excellent problem-solving skills and have a deep understanding of data analysis and interpretation. Candidates can prepare for the exam by taking online courses, attending training programs, and practicing using real-world data scenarios.
Google Professional-Data-Engineer exam is a certification offered by Google to validate the skills and expertise of data engineers. Professional-Data-Engineer exam is designed to test the ability of professionals to design, build, and maintain data processing systems that meet the needs of their organizations. It covers a wide range of topics, including data processing architecture, data modeling, data ingestion, and transformation.
NEW QUESTION # 116
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?
- A. create a third instance and sync the data from the two storage types via batch jobs
- B. the selection is final and you must resume using the same storage type
- C. run parallel instances where one is HDD and the other is SDD
- D. export the data from the existing instance and import the data into a new instance
Answer: D
Explanation:
Explanation
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd-
NEW QUESTION # 117
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. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
- B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
- C. Put the data into Google Cloud Storage.
- D. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
Answer: C
Explanation:
First rule of dataproc is to keep data in GCS.
NEW QUESTION # 118
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem
a. Which Google Cloud Platform product should you use?
- A. Cloud Datastore
- B. BigQuery
- C. Cloud Bigtable
- D. Cloud SQL
Answer: B
NEW QUESTION # 119
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?
- A. Cloud Bigtable
- B. Google BigQuery
- C. Google Cloud Datastore
- D. Google Cloud Storage
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION # 120
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)
- A. Increase the number of nodes in the Cloud Bigtable cluster
- B. Configure your Cloud Dataflow pipeline to use local execution
- C. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
- D. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
- E. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
Answer: D,E
NEW QUESTION # 121
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
- A. Convert the streaming insert code to batch load for individual messages.
- B. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
- C. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
- D. Re-write the application to load accumulated data every 2 minutes.
Answer: C
NEW QUESTION # 122
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is
powered by Google App Engine and server millions of users. How should you design the frontend to
respond to a database failure?
- A. Retry the query with exponential backoff, up to a cap of 15 minutes.
- B. Reduce the query frequency to once every hour until the database comes back online.
- C. Retry the query every second until it comes back online to minimize staleness of data.
- D. Issue a command to restart the database servers.
Answer: A
NEW QUESTION # 123
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?
- A. multi-keyed data with very low latency
- B. multi-keyed data with very high latency
- C. single-keyed data with very low latency
- D. single-keyed data with very high latency
Answer: C
Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 124
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?
- A. Write a job template in Cloud Dataproc to perform the data transfer.
- B. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
- C. Use Cloud Dataflow and write the data to Cloud Storage.
- D. Execute gsutil rsync from the on-premises servers.
Answer: D
NEW QUESTION # 125
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
- A. Cloud Bigtable
- B. Cloud SQL
- C. Cloud Datastore
- D. BigQuery
Answer: C
Explanation:
https://cloud.google.com/datastore/docs/concepts/overview
NEW QUESTION # 126
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?
- A. You can't run a TensorFlow program on your own computer using Cloud ML Engine .
- B. gcloud ml-engine local train
- C. gcloud ml-engine jobs submit training local
- D. gcloud ml-engine jobs submit training
Answer: B
Explanation:
gcloud ml-engine local train - run a Cloud ML Engine training job locally
This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.
This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration.
NEW QUESTION # 127
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud Datastore
- B. Cloud SQL
- C. Cloud BigTable
- D. BigQuery
Answer: C
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/
NEW QUESTION # 128
To give a user read permission for only the first three columns of a table, which access control method would you use?
- A. Authorized view
- B. Primitive role
- C. It's not possible to give access to only the first three columns of a table.
- D. Predefined role
Answer: A
Explanation:
Explanation
An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference: https://cloud.google.com/bigquery/docs/views#authorized-views
NEW QUESTION # 129
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?
- A. Disable caching by editing the report settings.
- B. Refresh your browser tab showing the visualizations.
- C. Clear your browser history for the past hour then reload the tab showing the virtualizations.
- D. Disable caching in BigQuery by editing table details.
Answer: A
NEW QUESTION # 130
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. Create schema designs that require atomicity across rows
- D. Avoid schema designs that require atomicity across rows
Answer: D
Explanation:
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
NEW QUESTION # 131
Which of these is not a supported method of putting data into a partitioned table?
- A. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
"Partitioned". - B. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- D. Create a partitioned table and stream new records to it every day.
Answer: A
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION # 132
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.
- A. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- B. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
- C. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
- D. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
- E. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
Answer: C,D
NEW QUESTION # 133
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
- A. Rowkey: dateColumn data: device_id, data_point
- B. Rowkey: date#data_pointColumn data: device_id
- C. Rowkey: device_idColumn data: date, data_point
- D. Rowkey: date#device_idColumn data: data_point
- E. Rowkey: data_pointColumn data: device_id, date
Answer: E
NEW QUESTION # 134
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use the WHERE_PARTITIONTIME pseudo column
- B. Use the TABLE_DATE_RANGE function
- C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- D. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
Answer: B
Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-app
NEW QUESTION # 135
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
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 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. Use the NOW () function in BigQuery to record the event's time.
- 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 automatically generated timestamp from Cloud Pub/Sub to order the data.
- D. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
Answer: B
NEW QUESTION # 136
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
- A. Masters, workers, and parameter servers
- B. Workers and parameter servers
- C. Workers
- D. Parameter servers
Answer: B
Explanation:
Explanation
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
NEW QUESTION # 137
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
- A. 500 GB
- B. 1 TB
- C. 500 TB
- D. 1 GB
Answer: B
Explanation:
Explanation
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options
NEW QUESTION # 138
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
- A. Train on the existing data while using the new data as your test set.
- B. Continuously retrain the model on a combination of existing data and the new data.
- C. Continuously retrain the model on just the new data.
- D. Train on the new data while using the existing data as your test set.
Answer: A
Explanation:
Explanation
https://cloud.google.com/automl-tables/docs/prepare
NEW QUESTION # 139
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
- A. Rowkey: date
Column data: device_id,data_point - B. Rowkey: data_point
Column data: device_id,date - C. Rowkey: device_id
Column data: date, data_point - D. Rowkey: date#device_id
Column data: data_point - E. Rowkey: date#data_point
Column data: device_id
Answer: D
Explanation:
The most common query is for all the data for a given device for a given day", rowkey should have info for both device and date.
NEW QUESTION # 140
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm.
To do this you need to add a synthetic feature. What should the value of that feature be?
- A. Y^2
- B. cos(X)
- C. X^2
- D. X^2+Y^2
Answer: B
NEW QUESTION # 141
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