Feb-2024 FREE Google Professional-Data-Engineer PRACTICE QUESTIONS AND ANSWERS UPDATES
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To be eligible for the exam, candidates should have a minimum of three years of experience in data engineering, as well as a thorough understanding of the Google Cloud Platform. They should also have hands-on experience in designing and implementing data processing systems using various Google Cloud tools and services, such as BigQuery, Cloud Dataflow, Cloud Storage, and Cloud Pub/Sub.
Google Professional-Data-Engineer certification exam consists of multiple-choice questions and performance-based tasks that simulate real-world scenarios. Professional-Data-Engineer exam is divided into four main domains: Designing data processing systems, building and operationalizing data processing systems, integrating data sources, and managing data processing infrastructure.
NEW QUESTION # 44
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform.
Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
- A. Cloud Bigtable
- B. BigQuery
- C. Cloud SQL
- D. Cloud Datastore
Answer: C
NEW QUESTION # 45
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?
- A. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
- B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.
- C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
- D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
Answer: C
NEW QUESTION # 46
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.
- A. conditional node
- B. worker node
- C. application node
- D. master node
Answer: D
Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my- cluster", the master-host-name would be "my-cluster-m".
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces
NEW QUESTION # 47
Which of the following are examples of hyperparameters? (Select 2 answers.)
- A. Number of nodes in each hidden layer
- B. Biases
- C. Number of hidden layers
- D. Weights
Answer: A,C
Explanation:
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 # 48
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 partitioned table called tracking_table and include a TIMESTAMP 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 table called tracking_table and include a DATE column.
Answer: A
NEW QUESTION # 49
Your company maintains a hybrid deployment with GCP, where analytics are performed on your
anonymized customer data. The data are imported to Cloud Storage from your data center through parallel
uploads to a data transfer server running on GCP. Management informs you that the daily transfers take
too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action
should you take?
- A. Increase the CPU size on your server.
- B. Increase the size of the Google Persistent Disk on your server.
- C. Increase your network bandwidth from Compute Engine to Cloud Storage.
- D. Increase your network bandwidth from your datacenter to GCP.
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION # 50
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?
- A. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
- B. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
- C. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
- D. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
Answer: C
NEW QUESTION # 51
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
- A. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
- B. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
- D. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
Answer: B
NEW QUESTION # 52
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
- A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
Configure the job to use non-default Compute Engine machine types when needed. - B. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
- C. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
- D. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
Answer: D
Explanation:
Dataflow is good with autoscaling and stackdriver to monitor CPU and Storage.
NEW QUESTION # 53
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
- A. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
- B. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
- C. Increase the size of your parquet files to ensure them to be 1 GB minimum.
- D. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
Answer: B
NEW QUESTION # 54
You work for a manufacturing plant that batches application log files together into a single log file once a
day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make
sure the log file in processed once per day as inexpensively as possible. What should you do?
- A. Manually start the Cloud Dataflow job each morning when you get into the office.
- B. Change the processing job to use Google Cloud Dataproc instead.
- C. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
- D. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
Answer: D
NEW QUESTION # 55
Case Study 1 - Flowlogistic
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 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 the common data encoded as Avro in Google Cloud Storage.
- B. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
- C. Store the common data in BigQuery as partitioned tables.
- D. Store the common data in BigQuery and expose authorized views.
Answer: D
Explanation:
DataProc can access data from Bigquery as well.
NEW QUESTION # 56
Which software libraries are supported by Cloud Machine Learning Engine?
- A. Theano and TensorFlow
- B. TensorFlow
- C. TensorFlow and Torch
- D. Theano and Torch
Answer: B
Explanation:
Explanation
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does
NEW QUESTION # 57
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 Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
- B. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
- C. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
- D. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.
Answer: A
NEW QUESTION # 58
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. BigQuery
- B. Cloud Bigtable
- C. Cloud Dataflow
- D. Cloud Datastore
Answer: A
Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.
NEW QUESTION # 59
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
- A. Trigger based on element size in bytes
- B. Trigger based on time
- C. Trigger that is a combination of other triggers
- D. Trigger based on element count
Answer: A
Explanation:
Explanation
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers
NEW QUESTION # 60
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity `Movie' the property `actors' and the property
`tags' have multiple values but the property `date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
- A. Option C
- B. Option A
- C. Option D
- D. Option B.
Answer: B
NEW QUESTION # 61
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
- A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
- B. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
- C. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application. - D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
Answer: B
Explanation:
The recommendation requires filtering based on several TB of data, therefore BigTable is the recommended option vs Cloud SQL which is limited to 10TB.
NEW QUESTION # 62
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. Use .fromQueryoperation to read specific fields from the table.
- B. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents
a single row in the table. - C. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
- D. Specify the TableReferenceobject in the code.
Answer: B
NEW QUESTION # 63
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create a one-hot column
- B. Create an embedding column
- C. Create a hash bucket
- D. Create a multi-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. Reference: https:// cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and- deep-model.html
NEW QUESTION # 64
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
- A. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
- B. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
- D. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
Answer: B
Explanation:
Explanation
NEW QUESTION # 65
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