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NEW QUESTION # 77
Which of the following are examples of hyperparameters? (Select 2 answers.)
- A. Number of nodes in each hidden layer
- B. Number of hidden layers
- C. Biases
- D. Weights
Answer: A,B
Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all.
They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview
NEW QUESTION # 78
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 Viewer
- B. Dataproc Worker
- C. Dataproc Editor
- D. Dataproc Runner
Answer: B
Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-
accounts#important_notes
NEW QUESTION # 79
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?
- A. Compute the hash value of each data entry, and compare it with all historical data.
- B. Assign global unique identifiers (GUID) to each data entry.
- C. Store each data entry as the primary key in a separate database and apply an index.
- D. Maintain a database table to store the hash value and other metadata for each data entry.
Answer: D
NEW QUESTION # 80
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below
4000, send an alert. - C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below
4000, send an alert. - D. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
Answer: B
NEW QUESTION # 81
You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?
- A. Match loan applicants with their social profiles to enable feature engineering.
- B. Increase the size of the dataset by collecting additional data.
- C. Remove the bias from the data and collect applications that have been declined loans.
- D. Train a linear regression to predict a credit default risk score.
Answer: D
NEW QUESTION # 82
You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?
- A. Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- B. Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.
- C. Schedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- D. Create a streaming Dataflow job that reads continually from the Pub/Sub topic and performs aggregations using tumbling windows.
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION # 83
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. None of the above
- B. Only Streaming Data Pipelines
- C. Both Batch and Streaming Data Pipelines
- D. Only Batch Data Pipelines
Answer: C
Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 84
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?
- A. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
- B. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
- C. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
- D. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
Answer: B
NEW QUESTION # 85
Scaling a Cloud Dataproc cluster typically involves ____.
- A. increasing or decreasing the number of worker nodes
- B. deleting applications from unused nodes periodically
- C. increasing or decreasing the number of master nodes
- D. moving memory to run more applications on a single node
Answer: A
Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1 ) increase the number of workers to make a job run faster
2 ) decrease the number of workers to save money
3 ) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage Reference: https://cloud.google.com/dataproc/docs/concepts/scaling-clusters
NEW QUESTION # 86
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 is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
- A. BigQuery and Cloud Storage
- B. Cloud Bigtable and Cloud SQL
- C. Cloud Datastore and Cloud Bigtable
- D. BigQuery and Cloud Bigtable
Answer: D
NEW QUESTION # 87
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?
- A. 3 categorical
- B. 3 continuous
- C. 2 continuous and 1 categorical
- D. 1 continuous and 2 categorical
Answer: C
Explanation:
Explanation
The columns can be grouped into two types-categorical and continuous columns:
A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.
A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.
Year of birth and income are continuous columns. Country is a categorical column.
You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.
Reference: https://www.tensorflow.org/tutorials/wide#reading_the_census_data
NEW QUESTION # 88
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?
- A. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.
- B. Submit duplicate pipelines in two different zones by using the -zone flag.
- C. Set the pipeline staging location as a regional Cloud Storage bucket.
- D. Specify a worker region by using the -region flag.
Answer: C
Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the -zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. Reference:
1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
3: Regional endpoints | Cloud Dataflow | Google Cloud
NEW QUESTION # 89
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution.
What should you do?
- A. Access historical data by using time travel in BigQuery.
- B. Migrate your data to multi-region BigQuery buckets.
- C. Export the data from BigQuery into a new table that excludes the corrupted data.
- D. Create a BigQuery table snapshot on a daily basis.
Answer: A
Explanation:
Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.
References:
Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps
NEW QUESTION # 90
You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily Your customers' information, such as their preferences, is hosted on a Cloud SQL for MySQL database Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers' information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time you want to keep the load on the Cloud SQL databases to a minimum. What should you do?
- A. Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.
- B. Create BigQuery connections to both Cloud SQL databases Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.
- C. Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.
- D. Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.
Answer: C
Explanation:
Datastream is a serverless Change Data Capture (CDC) and replication service that allows you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud Storage, Cloud SQL, and Pub/Sub. Datastream captures and delivers database changes in real-time, with minimal impact on the source database performance. Datastream also preserves the schema and data types of the source database, and automatically creates and updates the corresponding tables in BigQuery.
By using Datastream, you can replicate the required tables from both Cloud SQL databases to BigQuery, and keep them in sync with the source databases. This way, you can reduce the load on the Cloud SQL databases, as the marketing team can run their queries on the BigQuery tables instead of the Cloud SQL tables. You can also leverage the scalability and performance of BigQuery to query the customer behavioral data from Google Analytics and the customer information from the replicated tables. You can run the queries as frequently as needed, without worrying about the impact on the Cloud SQL databases.
Option A is not a good solution, as BigQuery federated queries allow you to query external data sources such as Cloud SQL databases, but they do not reduce the load on the source databases. In fact, federated queries may increase the load on the source databases, as they need to execute the query statements on the external data sources and return the results to BigQuery. Federated queries also have some limitations, such as data type mappings, quotas, and performance issues.
Option C is not a good solution, as creating a Dataproc cluster with Trino would require more resources and management overhead than using Datastream. Trino is a distributed SQL query engine that can connect to multiple data sources, such as Cloud SQL and BigQuery, and execute queries across them. However, Trino requires a Dataproc cluster to run, which means you need to provision, configure, and monitor the cluster nodes. You also need to install and configure the Trino connector for Cloud SQL and BigQuery, and write the queries in Trino SQL dialect. Moreover, Trino does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high.
Option D is not a good solution, as creating a job on Apache Spark with Dataproc Serverless would require more coding and processing power than using Datastream. Apache Spark is a distributed data processing framework that can read and write data from various sources, such as Cloud SQL and BigQuery, and perform complex transformations and analytics on them. Dataproc Serverless is a serverless Spark service that allows you to run Spark jobs without managing clusters. However, Spark requires you to write code in Python, Scala, Java, or R, and use the Spark connector for Cloud SQL and BigQuery to access the data sources. Spark also does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high. References: Datastream overview | Datastream | Google Cloud, Datastream concepts | Datastream | Google Cloud, Datastream quickstart | Datastream | Google Cloud, Introduction to federated queries | BigQuery | Google Cloud, Trino overview | Dataproc Documentation | Google Cloud, Dataproc Serverless overview | Dataproc Documentation | Google Cloud, Apache Spark overview | Dataproc Documentation | Google Cloud.
NEW QUESTION # 91
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. 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.
- 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. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull 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.
Answer: A
Explanation:
From Cloud SQL we can fetch the record on timestamp basis using where clause and it satisfies near real time.
NEW QUESTION # 92
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?
- A. Use feature engineering to add features for eyes, noses, and mouths to the input data.
- B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
- C. Use K-means Clustering to detect faces in the pixels.
- D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
Answer: B
Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines. Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview
NEW QUESTION # 93
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To prepare for the exam, prospective candidates can enroll in official GCP training courses or leverage various online resources such as practice exams, sample questions, and study materials. It's essential to have practical experience working with Google Cloud technologies to be able to pass the exam successfully. Professional-Data-Engineer exam's duration is two hours, and the passing score is 70 percent. Once a candidate completes the exam and earns the certification, they gain access to exclusive professional networking opportunities and recognition as an industry expert in data engineering.
Tested Material Used To Professional-Data-Engineer Test Engine: https://www.passleadervce.com/Google-Cloud-Certified/reliable-Professional-Data-Engineer-exam-learning-guide.html
Steps Necessary To Pass The Professional-Data-Engineer Exam: https://drive.google.com/open?id=1p9kYChm1UW4VpG1BY2IabrDujVl5E-8P