[Mar 26, 2022] Fully Updated Google Cloud Certified (Professional-Data-Engineer) Certification Sample Questions
Latest Google Professional-Data-Engineer Real Exam Dumps PDF
NEW QUESTION 116
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?
- A. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
- B. Use gsutil cp -Jto compress the content being uploaded to Cloud Storage
- C. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic
- D. Use Transfer Appliance to copy the data to Cloud Storage
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION 117
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- C. Use the TABLE_DATE_RANGE function
- D. Use the WHERE_PARTITIONTIME pseudo column
Answer: C
NEW QUESTION 118
Which of the following statements about Legacy SQL and Standard SQL is not true?
- A. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
- B. You need to set a query language for each dataset and the default is Standard SQL.
- C. Standard SQL is the preferred query language for BigQuery.
- D. One difference between the two query languages is how you specify fully-qualified table names (i.e.
table names that include their associated project name).
Answer: B
Explanation:
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released. In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql
NEW QUESTION 119
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. Redefine the schema by evenly distributing reads and writes across the row space of the table.
- B. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
- C. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
- D. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
Answer: A
Explanation:
https://cloud.google.com/bigtable/docs/performance#troubleshooting
If you find that you're reading and writing only a small number of rows, you might need to redesign your schema so that reads and writes are more evenly distributed.
NEW QUESTION 120
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. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
- B. Change the processing job to use Google Cloud Dataproc instead.
- C. Manually start the Cloud Dataflow job each morning when you get into the office.
- D. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
Answer: D
NEW QUESTION 121
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Userstable consisting of a FirstNamefield and a LastNamefield. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullNamefield consisting of the value of the FirstNamefield concatenated with a space, followed by the value of the LastNamefield for each employee. How can you make that data available while minimizing cost?
- A. Create a view in BigQuery that concatenates the FirstNameand LastNamefield values to produce the FullName.
- B. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastNameand FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
- C. Add a new column called FullNameto the Users table. Run an UPDATEstatement that updates the FullNamecolumn for each user with the concatenation of the FirstNameand LastNamevalues.
- D. Create a Google Cloud Dataflow job that queries BigQuery for the entire Userstable, concatenates the FirstNamevalue and LastNamevalue for each user, and loads the proper values for FirstName, LastName, and FullNameinto a new table in BigQuery.
Answer: D
NEW QUESTION 122
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- B. Create a cron schedule in Cloud Dataprep.
- C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
- D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
Answer: C
Explanation:
Explanation
NEW QUESTION 123
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. Cloud Pub/Sub
- C. Firebase Cloud Messaging
- D. Cloud Storage
Answer: A
NEW QUESTION 124
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. HDFS with Hive
- B. MySQL
- C. HBase
- D. Cassandra
- E. MongoDB
- F. Redis
Answer: A,C,E
NEW QUESTION 125
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 Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
- C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.
- D. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to `none' using a Cloud Dataprep job.
Answer: D
NEW QUESTION 126
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud Datastore
- B. Cloud BigTable
- C. Cloud SQL
- D. BigQuery
Answer: B
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/business-intelligence/
NEW QUESTION 127
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?
- A. Create and share a new dataset and view that provides the aggregate results.
- B. Create and share a new dataset and table that contains the aggregate results.
- C. Create and share an authorized view that provides the aggregate results.
- D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
Answer: D
Explanation:
Explanation/Reference: https://cloud.google.com/bigquery/docs/access-control
NEW QUESTION 128
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. Grant the user access to Google Cloud Shell.
- B. Host a visualization tool on a VM on Google Compute Engine.
- 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: A
NEW QUESTION 129
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 2 answers.
- A. Use Cloud Deployment Manager to automate access provision.
- B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- C. 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.
- D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- E. Introduce resource hierarchy to leverage access control policy inheritance.
Answer: A,D
Explanation:
Explanation/Reference:
NEW QUESTION 130
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. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- B. 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.
- C. Introduce data compression for each file to increase the rate file of file transfer.
- D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
- E. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
Answer: A,E
NEW QUESTION 131
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)
- A. Ensure each table is included in a dataset for a region.
- B. Adjust the settings for each dataset to allow a related region-based security group view access.
- C. Ensure all the tables are included in global dataset.
- D. Adjust the settings for each table to allow a related region-based security group view access.
- E. Adjust the settings for each view to allow a related region-based security group view access.
Answer: A,E
NEW QUESTION 132
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?
- A. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
- B. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
- C. Create a table in BigQuery, and append the new samples for CPU and memory to the table
- D. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
Answer: B
NEW QUESTION 133
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Professional-Data-Engineer Practice Test Questions Updated 253 Questions: https://drive.google.com/open?id=1VvdPK2ExxSmo4TF5O8blgVBad0kk5cry
