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Google Professional-Data-Engineer certification has become a must-have for data engineering professionals who work with Google Cloud. Google Certified Professional Data Engineer Exam certification validates their knowledge and skills in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification also helps professionals to stand out in a competitive job market and advance their careers.
NEW QUESTION # 15
You are training a spam classifier. You notice that you are overfitting the training dat
a. Which three actions can you take to resolve this problem? (Choose three.)
- A. Use a smaller set of features
- B. Reduce the number of training examples
- C. Get more training examples
- D. Increase the regularization parameters
- E. Decrease the regularization parameters
- F. Use a larger set of features
Answer: C,E,F
NEW QUESTION # 16
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create a hash bucket
- B. Create an embedding column
- C. Create a multi-hot column
- D. Create a one-hot column
Answer: B
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding
column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
NEW QUESTION # 17
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?
- A. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
- B. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
- C. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
- D. Assign the users/groups data viewer access at the table level for each table
Answer: D
NEW QUESTION # 18
Your company built a TensorFlow neural-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. Threading
- B. Dropout Methods
- C. Serialization
- D. Dimensionality Reduction
Answer: B
Explanation:
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
NEW QUESTION # 19
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.
You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?
- A. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- B. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 20
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
- A. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
- B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull
- C. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
- D. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
Answer: C
Explanation:
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
NEW QUESTION # 21
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 need to compose visualizations for operations teams with the following requirements:
* The report must include telemetry data from all 50,000 installations for the most resent 6 weeks (sampling once every minute).
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
Which approach meets the requirements?
- A. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
- B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
- C. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
- D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
Answer: D
NEW QUESTION # 22
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. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
- B. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
- C. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
- D. Use the NOW () function in BigQuery to record the event's time.
Answer: A
Explanation:
Topic 3, 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.
NEW QUESTION # 23
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?
- A. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
- B. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
- C. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
- D. Redefine the schema by evenly distributing reads and writes across the row space of the table.
Answer: D
NEW QUESTION # 24
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.
- A. unique key
- B. master key
- C. primary key
- D. row key
Answer: D
Explanation:
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.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 25
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?
- A. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table. - B. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts - C. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table. - D. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
Answer: A
Explanation:
Cloud DLP is a service that helps you discover, classify, and protect your sensitive data. It supports various de-identification techniques, such as masking, redaction, tokenization, and encryption. Format-preserving encryption (FPE) with FFX is a technique that encrypts sensitive data while preserving its original format and length. This allows you to join the encrypted data on the same field without revealing the actual values. FPE with FFX also supports partial encryption, which means you can encrypt only a portion of the data, such as the domain name of an email address. By using Cloud DLP to de-identify the email field with FPE with FFX, you can ensure that the analysts can join the booking and user profile data on the email field without accessing the PII. You can create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP, which allows you to specify the fields and the de-identification transformations to apply to them. You can then load the de-identified data into a BigQuery table for analysis. References:
* De-identify sensitive data | Cloud Data Loss Prevention Documentation
* Format-preserving encryption with FFX | Cloud Data Loss Prevention Documentation
* De-identify and re-identify data with the Cloud DLP API
* De-identify data in a pipeline
NEW QUESTION # 26
These 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. 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.
- B. 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.
- C. 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.
- D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.
- E. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables. Replicate external Hive tables to the native ones.
Answer: A,B
NEW QUESTION # 27
Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
* The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
* Support for publish/subscribe semantics on hundreds of topics
* Retain per-key ordering
Which system should you choose?
- A. Apache Kafka
- B. Firebase Cloud Messaging
- C. Cloud Pub/Sub
- D. Cloud Storage
Answer: A
Explanation:
These are the functionalities which are currently lagging/not-available with Pub/Sub.
NEW QUESTION # 28
You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes.
What should you do?
- A. Use session windows with a 15-minute gap duration.
- B. Use session windows with a 30-mmute gap duration.
- C. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
- D. Use hopping windows with a 15-mmute window, and a thirty-minute period.
Answer: A
Explanation:
Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary. Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity. Reference:
Windowing concepts
Session windows
Windowing in Dataflow
NEW QUESTION # 29
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery.
Your access pattern is based on recent data tittered by location_id and device_version with the following query:
You want to optimize your queries for cost and performance. How should you structure your data?
- A. Partition table data by create_date, location_id and device_version
- B. Cluster table data by create_date location_id and device_version
- C. Cluster table data by create_date, partition by location and device_version
- D. Partition table data by create_date cluster table data by tocation_id and device_version
Answer: B
NEW QUESTION # 30
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 wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
- A. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
- B. Store the common data in BigQuery and expose authorized views.
- C. Store the common data in BigQuery as partitioned tables.
- D. Store the common data encoded as Avro in Google Cloud Storage.
Answer: B
NEW QUESTION # 31
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds.
You use the following query to generate predictions:
SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features).
How should you create the ML pipeline?
- A. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
- B. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
- C. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
- D. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
Answer: D
NEW QUESTION # 32
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:
What is the most likely cause of the delay for this query?
- A. The [myproject:mydataset.mytable] table has too many partitions
- B. Either the state or the city columns in the [myproject:mydataset.mytable] table have too manyNULL values
- C. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
- D. Users are running too many concurrent queries in the system
Answer: D
NEW QUESTION # 33
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. Refresh your browser tab showing the visualizations.
- B. Clear your browser history for the past hour then reload the tab showing the virtualizations.
- C. Disable caching in BigQuery by editing table details.
- D. Disable caching by editing the report settings.
Answer: D
Explanation:
https://support.google.com/datastudio/answer/7020039?hl=en
NEW QUESTION # 34
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
- A. The wide model is used for generalization, while the deep model is used for memorization.
- B. A good use for the wide and deep model is a recommender system.
- C. The wide model is used for memorization, while the deep model is used for generalization.
- D. A good use for the wide and deep model is a small-scale linear regression problem.
Answer: B,C
Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
NEW QUESTION # 35
......
Preparation Process
To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.
Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.
Focus on Professional-Data-Engineer All-in-One Exam Guide For Quick Preparation: https://dumpstorrent.actualpdf.com/Professional-Data-Engineer-real-questions.html
