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Data Engineering on Google Cloud course
It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:
- Designing and building data processing systems on the Google Cloud Platform
- Predicting machine models using TensorFlow and Cloud ML
- Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
- Influencing unstructured data using ML APIs on Cloud Dataproc
- Enable insights from streaming data
To become a Google Certified Professional Data Engineer, candidates need to pass the Professional-Data-Engineer exam. Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions, and it is intended for professionals who have a minimum of three years of industry experience in data engineering. Professional-Data-Engineer exam duration is 2 hours, and the passing score is 70%. Professional-Data-Engineer exam fee is $200, and it can be taken at a testing center or remotely online.
NEW QUESTION # 11
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want
to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-
level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for
other projects is assigned to those projects. What should they do?
- A. Create and share an authorized view that provides the aggregate results.
- B. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
- C. Create and share a new dataset and table that contains the aggregate results.
- D. Create and share a new dataset and view that provides the aggregate results.
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/bigquery/docs/access-control
NEW QUESTION # 12
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for
sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows
your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube
channels log data. How should you set up the log data transfer into Google Cloud?
- A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
storage bucket as a final destination. - B. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-
Regional storage bucket as a final destination. - C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional
storage bucket as a final destination. - D. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as
a final destination.
Answer: D
NEW QUESTION # 13
Which of the following is not possible using primitive roles?
- A. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
- B. Give a user access to view all datasets in a project, but not run queries on them.
- C. Give GroupA owner access and GroupB editor access for all datasets in a project.
- D. Give UserA owner access and UserB editor access for all datasets in a project.
Answer: B
Explanation:
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
NEW QUESTION # 14
Which action can a Cloud Dataproc Viewer perform?
- A. Submit a job.
- B. List the jobs.
- C. Delete a cluster.
- D. Create a cluster.
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.
Reference:
https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_ope rations_summary
NEW QUESTION # 15
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.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)
- A. Adjust the settings for each table to allow a related region-based security group view access.
- B. Adjust the settings for each dataset to allow a related region-based security group view access.
- C. Ensure each table is included in a dataset for a region.
- D. Ensure all the tables are included in global dataset.
- E. Adjust the settings for each view to allow a related region-based security group view access.
Answer: B,C
NEW QUESTION # 16
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- B. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- C. Load the data every 30 minutes into a new partitioned table in BigQuery.
- D. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
Answer: C
NEW QUESTION # 17
MJTelco Case Study
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create a table called tracking_table and include a DATE column.
- B. Create a table called tracking_table with a TIMESTAMP column to represent the day.
- C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- D. Create a partitioned table called tracking_table and include a TIMESTAMP column.
Answer: D
NEW QUESTION # 18
When a Cloud Bigtable node fails, ____ is lost.
- A. the time dimension
- B. the last transaction
- C. all data
- D. no data
Answer: D
Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost
NEW QUESTION # 19
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.
Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Convert all daily log tables into date-partitioned tables
- B. Create separate views to cover each month, and query from these views
- C. Convert the sharded tables into a single partitioned table
- D. Enable query caching so you can cache data from previous months
Answer: A
NEW QUESTION # 20
What is the HBase Shell for Cloud Bigtable?
- A. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
- B. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.
- C. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
- D. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
Answer: A
Explanation:
Explanation
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell
NEW QUESTION # 21
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?
- A. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
- B. Perform hyperparameter tuning
- C. Deploy the model and measure the real-world AUC; it's always higher because of generalization
- D. Train a classifier with deep neural networks, because neural networks would always beat SVMs
Answer: B
NEW QUESTION # 22
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
- A. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
- B. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
- C. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
- D. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
Answer: B
NEW QUESTION # 23
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)
- A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- B. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- E. Use Cloud Deployment Manager to automate access provision.
Answer: B,E
NEW QUESTION # 24
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 Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
- B. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
- C. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
- D. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
Answer: C
NEW QUESTION # 25
Your neural network model is taking days to train. You want to increase the training speed. What can you
do?
- A. Subsample your test dataset.
- B. Subsample your training dataset.
- C. Increase the number of layers in your neural network.
- D. Increase the number of input features to your model.
Answer: C
Explanation:
Explanation/Reference:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-
9f5d1c6f407d
NEW QUESTION # 26
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street
Tim,553 Y street
Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
- A. Sink API
- B. Source API
- C. ParDo
- D. Data extraction
Answer: C
Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each
element in your PCollection.
NEW QUESTION # 27
You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?
- A. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.
- B. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
- C. Store and process the entire dataset in BigQuery.
- D. Store and process the entire dataset in Cloud Bigtable.
Answer: A
NEW QUESTION # 28
MJTelco Case Study
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's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
- A. The maximum number of workers
- B. The zone
- C. The number of workers
- D. The disk size per worker
Answer: B
NEW QUESTION # 29
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. Supervised learning to determine which transactions are most likely to be fraudulent.
- B. Clustering to divide the transactions into N categories based on feature similarity.
- C. Unsupervised learning to predict the location of a transaction.
- D. Supervised learning to predict the location of a transaction.
- E. Reinforcement learning to predict the location of a transaction.
- F. Unsupervised learning to determine which transactions are most likely to be fraudulent.
Answer: B,D,F
NEW QUESTION # 30
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Create a cron schedule in Cloud Dataprep.
- B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
- C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
Answer: B
NEW QUESTION # 31
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. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
- D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
Answer: D
NEW QUESTION # 32
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)
- A. You expect future mutations to have different features from the mutated samples in the database.
- B. There are roughly equal occurrences of both normal and mutated samples in the database.
- C. You already have labels for which samples are mutated and which are normal in the database.
- D. There are very few occurrences of mutations relative to normal samples.
- E. You expect future mutations to have similar features to the mutated samples in the database.
Answer: D,E
Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection
NEW QUESTION # 33
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
- A. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
- B. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
- C. Specify the Tableobject in the code.
- D. Use .fromQuery operation to read specific fields from the table.
Answer: A
NEW QUESTION # 34
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 determine which transactions are most likely to be fraudulent.
- B. Reinforcement learning to predict the location of a transaction.
- C. Clustering to divide the transactions into N categories based on feature similarity.
- D. Unsupervised learning to predict the location of a transaction.
- E. Supervised learning to predict the location of a transaction.
- F. Unsupervised learning to determine which transactions are most likely to be fraudulent.
Answer: B,C,F
NEW QUESTION # 35
You want to optimize your queries for cost and performance. How should you structure your data?
- A. Cluster table data by create_date partition by locationed and device_version
- B. Cluster table data by create_date location_id and device_version
- C. Partition table data by create_date, location_id and device_version
- D. Partition table data by create_date cluster table data by location_Id and device_version
Answer: D
NEW QUESTION # 36
......
Google Certified Professional Data Engineer exam is a comprehensive test that evaluates the candidate's proficiency in a variety of areas related to data processing systems. Professional-Data-Engineer exam covers topics such as the design and implementation of data processing systems, the management of data storage and processing systems, and the optimization of data processing workflows. Professional-Data-Engineer exam is designed to be challenging and requires a deep understanding of data processing systems and the Google Cloud Platform.
Pass Professional-Data-Engineer Tests Engine pdf - All Free Dumps: https://passtorrent.testvalid.com/Professional-Data-Engineer-valid-exam-test.html