Professional-Data-Engineer Practice Exam Tests Latest Updated on Apr-2023
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What is the duration, language, and format of Google Professional Data Engineer Exam
- Length of Examination: 120 minutes
- Number of Questions: 50-60
- Language: English (U.S.), Japanese, Spanish, and Portuguese
NEW QUESTION 11
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 two.)
- A. 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.
- B. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- 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 store the data in a Cloud Storage bucket using Nearline or Coldline class.
- E. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
Answer: B,D
Explanation:
https://cloud.google.com/datastore/docs/export-import-entities
NEW QUESTION 12
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
- A. Workers and parameter servers
- B. Workers
- C. Parameter servers
- D. Masters, workers, and parameter servers
Answer: A
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.
NEW QUESTION 13
Which of these operations can you perform from the BigQuery Web UI?
- A. Upload multiple files using a wildcard.
- B. Load data with nested and repeated fields.
- C. Upload a file in SQL format.
- D. Upload a 20 MB file.
Answer: B
Explanation:
You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command.
Reference: https://cloud.google.com/bigquery/loading-data
NEW QUESTION 14
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. 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.
- B. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- 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. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
Answer: C,D
NEW QUESTION 15
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 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.
- B. 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.
- C. 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. - D. 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.
Answer: B
NEW QUESTION 16
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
- A. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
- B. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
- C. Increase the size of your parquet files to ensure them to be 1 GB minimum.
- D. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
Answer: A
NEW QUESTION 17
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?
- A. Cloud Datastore
- B. Cloud Spanner
- C. Cloud SQL
- D. Cloud Bigtable
Answer: C
NEW QUESTION 18
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
* You will batch-load the posts once per day and run them through the Cloud Natural Language API.
* You will extract topics and sentiment from the posts.
* You must store the raw posts for archiving and reprocessing.
* You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?
- A. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
- B. Store the social media posts and the data extracted from the API in Cloud SQL.
- C. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
- D. Store the social media posts and the data extracted from the API in BigQuery.
Answer: A
Explanation:
Explanation
NEW QUESTION 19
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
* No interaction by the user on the site for 1 hour
* Has added more than $30 worth of products to the basket
* Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?
- A. Use a sliding time window with a duration of 60 minutes.
- B. Use a global window with a time based trigger with a delay of 60 minutes.
- C. Use a fixed-time window with a duration of 60 minutes.
- D. Use a session window with a gap time duration of 60 minutes.
Answer: D
NEW QUESTION 20
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. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
- B. Convert the streaming insert code to batch load for individual messages.
- 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: D
NEW QUESTION 21
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 data. Which three machine learning applications can you use?
(Choose three.)
- A. Supervised learning to predict the location of a transaction.
- B. Unsupervised learning to predict the location of a transaction.
- C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- D. Reinforcement learning to predict the location of a transaction.
- E. Clustering to divide the transactions into N categories based on feature similarity.
- F. Supervised learning to determine which transactions are most likely to be fraudulent.
Answer: C,D,E
Explanation:
Explanation/Reference:
NEW QUESTION 22
Which software libraries are supported by Cloud Machine Learning Engine?
- A. TensorFlow
- B. Theano and TensorFlow
- C. Theano and Torch
- D. TensorFlow and Torch
Answer: A
Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions
about new data.
NEW QUESTION 23
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 the same zone.
- C. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
- D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
Answer: B
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 24
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 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 Load Balancing, Cloud Dataflow, and Cloud Storage
- B. Cloud Dataflow, Cloud SQL, and Cloud Storage
- C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
- E. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
Answer: E
Explanation:
Explanation
NEW QUESTION 25
Which action can a Cloud Dataproc Viewer perform?
- A. Delete a cluster.
- B. List the jobs.
- C. Create a cluster.
- D. Submit a job.
Answer: B
Explanation:
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
NEW QUESTION 26
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?
- A. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
- B. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
- C. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
- D. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
Answer: B
NEW QUESTION 27
......
The benefit of obtaining the Google Professional Data Engineer Exam Certification
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:
Design Data Processing Systems
- Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
- Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
- Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
- Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
Google Cloud Certified Free Certification Exam Material from TopExamCollection with 270 Questions: https://examcompass.topexamcollection.com/Professional-Data-Engineer-vce-collection.html
