Prepare with latest Google GCP-DE Pass-King Actual Torrent

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 "city.person".
• B. Add ", UNNEST(person)" before the WHERE clause.
• C. Change "person" to "person.city".
• D. Add ", UNNEST(city)" before the WHERE clause.

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. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
• B. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
• C. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
• D. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
• E. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

• A. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
• B. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
• C. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
• D. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

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 schem a. Which Google Cloud Platform product should you use?

• A. Cloud SQL
• B. BigQuery
• C. Cloud Datastore
• D. Cloud Bigtable

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

• A. Create an application that provides an AP
• B. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
• C. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• E. Create an application that writes to a Cloud SQL database to store the dat
• F. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.