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NEW QUESTION # 49
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 TableRowobjects, where each element in the PCollectionrepresents
a single row in the table. - B. Specify the TableReferenceobject in the code.
- C. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
- D. Use .fromQueryoperation to read specific fields from the table.
Answer: A
NEW QUESTION # 50
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud.
Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?
- A. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
- B. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
- C. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
- D. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.
Answer: B
NEW QUESTION # 51
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. 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.
- 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 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. - C. 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.
- D. 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.
Answer: A
NEW QUESTION # 52
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?
- A. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
- B. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery
- C. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
- D. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
Answer: B
NEW QUESTION # 53
You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?
- A. Use Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.
- B. Use Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.
- C. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.
- D. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.
Answer: C
Explanation:
Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project. Reference: https://cloud.google.com/composer/docs/how-to/managing/configuring-shared-vpc
NEW QUESTION # 54
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. Only Batch Data Pipelines
- B. Both Batch and Streaming Data Pipelines
- C. Only Streaming Data Pipelines
- D. None of the above
Answer: B
Explanation:
Explanation
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 55
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?
- A. Create a view on the table to present to the virtualization tool.
- B. Create an additional table with only the necessary columns.
- C. Export the data into a Google Sheet for virtualization.
- D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
Answer: A
NEW QUESTION # 56
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/ Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket.
The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?
- A. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
- B. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
- C. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
- D. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
Answer: A
Explanation:
Increase in number of undelivered messages shows that the messages are not getting subscribed.
NEW QUESTION # 57
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. Deploy the model and measure the real-world AUC; it's always higher because of generalization
- B. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
- C. Perform hyperparameter tuning
- D. Train a classifier with deep neural networks, because neural networks would always beat SVMs
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION # 58
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 hopping windows with a 15-mmute window, and a thirty-minute period.
- B. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
- C. Use session windows with a 30-mmute gap duration.
- D. Use session windows with a 15-minute gap duration.
Answer: D
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 # 59
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. Supervised learning to determine which transactions are most likely to be fraudulent.
- B. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- C. Supervised learning to predict the location of a transaction.
- D. Unsupervised learning to predict the location of a transaction.
- E. Clustering to divide the transactions into N categories based on feature similarity.
- F. Which three machine learning applications can you use? (Choose three.)
- G. Reinforcement learning to predict the location of a transaction.
Answer: A,B,C
NEW QUESTION # 60
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 SQL for storage. Add secondary indexes to support query patterns.
- B. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- C. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
Answer: C
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 61
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?
- A. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
- B. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
- C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
- D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
Answer: D
Explanation:
https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions
NEW QUESTION # 62
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. Use Cloud Deployment Manager to automate access provision.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- E. 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.
Answer: C,D
Explanation:
Google suggests that we should provide access by following google hierarchy and groups for users with similar roles.
NEW QUESTION # 63
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. Use Cloud Dataflow to transform data to support query patterns.
- B. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
- C. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
Answer: D
Explanation:
Spanner allows transaction tables to scale horizontally and secondary indexes for range queries.
NEW QUESTION # 64
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Google Professional-Data-Engineer exam is a certification offered by Google to those who want to showcase their expertise in designing, building, and maintaining data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is designed for professionals who have a deep understanding of data engineering and can leverage Google Cloud technologies to create scalable and efficient data pipelines. Professional-Data-Engineer exam assesses the candidate's ability to design data processing systems, build and operationalize data pipelines, and manage and monitor data processing infrastructure.
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