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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:
- Introduction to Building Batch Data Pipelines
- Advanced BigQuery Performance and Functionality
- Cloud Dataflow Streaming Features
- Introduction to Data Engineering
- Introduction to Processing Streaming Data
- Building a Data Warehouse
- Prebuilt ML Models APIs for Unsaturated Data
- Custom Model building Using SQL in BigQuery ML
- Serverless Data Processing with Cloud Dataflow
- Creating a Data Lake
- Serverless Messaging Using Cloud Sub/Pub
- Production ML Pipelines and use of Kubeflow
- Custom Model building Utilizing Cloud AutoML
- Big Data Analytics with Cloud Al Platform Notebook
- Bigtable Streaming Features and High-Throughput BigQuery
- Performing Spark on Cloud Dataproc
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.
NEW QUESTION 136
Which of these is NOT a way to customize the software on Dataproc cluster instances?
- A. Set initialization actions
- B. Log into the master node and make changes from there
- C. Configure the cluster using Cloud Deployment Manager
- D. Modify configuration files using cluster properties
Answer: C
Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.
When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up.
[https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster- properties
NEW QUESTION 137
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 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. - C. 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.
- 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: C
NEW QUESTION 138
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-
Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and
durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your
requirements? (Choose three.)
- A. MySQL
- B. HBase
- C. HDFS with Hive
- D. Redis
- E. MongoDB
- F. Cassandra
Answer: B,C,E
NEW QUESTION 139
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
- A. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
- B. Re-write the application to load accumulated data every 2 minutes.
- C. Convert the streaming insert code to batch load for individual messages.
- D. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
Answer: A
NEW QUESTION 140
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.
- A. SOCKS
- B. VPN
- C. HTTPS
- D. HTTP
Answer: A
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web- interfaces#interfaces
NEW QUESTION 141
You are designing a basket abandonment system for an ecommerce company. The system will send a
message to a user based on these rules:
No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should
you design the pipeline?
- A. Use a fixed-time window with a duration of 60 minutes.
- B. Use a session window with a gap time duration of 60 minutes.
- C. Use a global window with a time based trigger with a delay of 60 minutes.
- D. Use a sliding time window with a duration of 60 minutes.
Answer: C
NEW QUESTION 142
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
- A. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
- B. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
- C. Add capacity (memory and disk space) to the database server by the order of 200.
- D. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
Answer: D
NEW QUESTION 143
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat The user account information: Name, address, preferred meal times The order information: When orders are made, from where, to whom The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
- A. Cloud Datastore
- B. BigQuery
- C. Cloud Bigtable
- D. Cloud SQL
Answer: B
NEW QUESTION 144
Which of these is not a supported method of putting data into a partitioned table?
- A. Create a partitioned table and stream new records to it every day.
- B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- C. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
Answer: D
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION 145
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by
10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)
- A. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
- B. Introduce data compression for each file to increase the rate file of file transfer.
- C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- D. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.
- E. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
Answer: C,D
NEW QUESTION 146
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. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION 147
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? (Choose two.)
- A. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
- B. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
- C. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- D. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
- E. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
Answer: D,E
NEW QUESTION 148
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required. You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
- A. MySQL
- B. HBase
- C. HDFS with Hive
- D. Redis
- E. MongoDB
- F. Cassandra
Answer: B,C,E
NEW QUESTION 149
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.
- A. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
- B. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- C. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
- D. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
- E. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
Answer: D,E
NEW QUESTION 150
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 export, compress, and store the data in Cloud Storage.
- 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 separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
- D. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
Answer: B
Explanation:
Explanation
NEW QUESTION 151
Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?
- A. Get the identity and access management IIAM) policy of each table
- B. Use Google Stackdriver Audit Logs to review data access.
- C. Use the Google Cloud Billing API to see what account the warehouse is being billed to.
- D. Use Stackdriver Monitoring to see the usage of BigQuery query slots.
Answer: D
NEW QUESTION 152
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
- A. An hourly watermark
- B. A processing time trigger
- C. The with Allowed Lateness method
- D. An event time trigger
Answer: B
Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time ?the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers
NEW QUESTION 153
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks.
She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?
- A. Host a visualization tool on a VM on Google Compute Engine.
- B. Grant the user access to Google Cloud Shell.
- C. Run a local version of Jupiter on the laptop.
- D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION 154
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?
- A. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
- B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
- C. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
- D. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
Answer: C
NEW QUESTION 155
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Change "person" to "person.city".
- B. Change "person" to "city.person".
- C. Add ", UNNEST(person)" before the WHERE clause.
- D. Add ", UNNEST(city)" before the WHERE clause.
Answer: C
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results
NEW QUESTION 156
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems.
What should you do?
- A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review. - B. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- C. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
- D. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
Answer: A
NEW QUESTION 157
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?
- A. Cloud Bigtable
- B. Google BigQuery
- C. Google Cloud Storage
- D. Google Cloud Datastore
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series
NEW QUESTION 158
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How to Prepare For Google Professional Data Engineer Exam
Preparation Guide for Google Professional Data Engineer Exam
Introduction to Google Professional Data Engineer Exam
Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets GoogleâÂÂs proficiency standards.
The Professional Data Engineer exam assesses your ability to:
- Design data processing systems
- Ensure solution quality
- Operationalize machine learning models
- Build and operationalize data processing systems
Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.
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