Pass Professional-Data-Engineer Exam - Real Test Engine PDF with 270 Questions
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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;
- 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.
- 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;
- 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;
The Google Professional-Data-Engineer certification is a globally recognized credential that demonstrates your ability to design, build, and maintain efficient data processing systems on the Google Cloud Platform. The certification exam covers a wide range of topics, such as designing data processing systems, building and maintaining data structures and databases, analyzing data, and optimizing data processing systems for performance and cost-effectiveness.
The Google Professional-Data-Engineer Certification Exam is intended for data engineers, data scientists, and other IT professionals who want to demonstrate their proficiency in Google Cloud Platform data engineering technologies. The exam covers topics such as design and implementation of data processing systems, building and maintaining databases, data analysis and visualization, and machine learning implementation.
NEW QUESTION # 141
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. Clear your browser history for the past hour then reload the tab showing the virtualizations.
- C. Refresh your browser tab showing the visualizations.
- D. Disable caching in BigQuery by editing table details.
Answer: A
Explanation:
https://support.google.com/datastudio/answer/7020039?hl=en
NEW QUESTION # 142
You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?
- A. Logistic classification
- B. Feedforward neural network
- C. Linear regression
- D. Recurrent neural network
Answer: C
NEW QUESTION # 143
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.
- A. master key
- B. row key
- C. primary key
- D. unique key
Answer: B
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.
NEW QUESTION # 144
Which of these operations can you perform from the BigQuery Web UI?
- A. Upload a 20 MB file.
- B. Upload multiple files using a wildcard.
- C. Upload a file in SQL format.
- D. Load data with nested and repeated fields.
Answer: D
Explanation:
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 # 145
Why do you need to split a machine learning dataset into training data and test data?
- A. So you can use one dataset for a wide model and one for a deep model
- B. To make sure your model is generalized for more than just the training data
- C. To allow you to create unit tests in your code
- D. So you can try two different sets of features
Answer: B
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/
NEW QUESTION # 146
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?
- A. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query
- B. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
- C. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- D. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
Answer: B
Explanation:
you can use dataprep for continuously changing target schema
In general, a target consists of the set of information required to define the expected data in a dataset. Often referred to as a "schema," this target schema information can include:
Names of columns
Order of columns
Column data types
Data type format
Example rows of data
A dataset associated with a target is expected to conform to the requirements of the schema. Where there are differences between target schema and dataset schema, a validation indicator (or schema tag) is displayed.
https://cloud.google.com/dataprep/docs/html/Overview-of-RapidTarget_136155049
NEW QUESTION # 147
You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?
- A. Manually start the Cloud Dataflow job each morning when you get into the office.
- B. Change the processing job to use Google Cloud Dataproc instead.
- C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
- D. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
Answer: C
NEW QUESTION # 148
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems.
What should you do?
- A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review. - B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
- C. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
Answer: D
NEW QUESTION # 149
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?
- A. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
- B. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
- C. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
- D. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
Answer: B
NEW QUESTION # 150
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. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
- B. Re-write the application to load accumulated data every 2 minutes.
- C. Convert the streaming insert code to batch load for individual messages.
- D. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
Answer: A
Explanation:
Explanation
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage
NEW QUESTION # 151
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc?
(Choose two.)
- A. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables.
Replicate external Hive tables to the native ones. - B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.
- C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.
- D. Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.
- E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.
Answer: B,E
Explanation:
HDFS lies on datanode, data on masternode needs to be copied on datanode.
NEW QUESTION # 152
What are two of the benefits of using denormalized data structures in BigQuery?
- A. Reduces the amount of data processed, reduces the amount of storage required
- B. Increases query speed, makes queries simpler
- C. Reduces the amount of data processed, increases query speed
- D. Reduces the amount of storage required, increases query speed
Answer: B
Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses. Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION # 153
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
- A. Dimensionality Reduction
- B. Dropout Methods
- C. Threading
- D. Serialization
Answer: B
Explanation:
Explanation
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505
Topic 1, 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.
NEW QUESTION # 154
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?
- A. Dataproc Worker
- B. Dataproc Runner
- C. Dataproc Editor
- D. Dataproc Viewer
Answer: A
Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
NEW QUESTION # 155
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
- A. FTP connection
- B. Special browser
- C. SSH tunnel
- D. VPN connection
Answer: C
Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
NEW QUESTION # 156
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