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Total 472 Questions | Updated On: Nov 19, 2024

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Question 1

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud

Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a

Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to

run additional transformation jobs in BigQuery The transformation jobs are different for every table These

jobs might take hours to complete You need to determine the most efficient and maintainable workflow to

process hundreds of tables and provide the freshest data to your end users. What should you do?

A : 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage. Dataproc. and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline3 Schedule the DAG to run hourly

B : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators.2 Create a separate DAG for each table that needs to go through the pipeline3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage, Dataproc. and BigQuery operators2 Create a separate DAG for each table that needs to go through the pipeline3 Schedule the DAGs to run hourly

D : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline.3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Answer: B

Question 2

Your new customer has requested daily reports that show their net consumption of Google Cloud compute

resources and who used the resources. You need to quickly and efficiently generate these daily reports. What

should you do?

A : Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.

B : Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.

C : Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.

D : Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

Answer: B

Question 3

A manufacturer of delivery drones is implementing a new data analysis pipeline to detect part failures before they occur. The drones have multiple sensors that send performance and environment data to an analytics pipeline. Currently, data is sent to a REST API endpoint. The REST API endpoint that receives data cannot always keep up with the pace data is arriving. When that happens, data is lost. Machine learning engineers have asked you to change the ingestion process to reduce this data loss. What would you do?

A : Write data to a Cloud Pub/Sub topic instead of a REST API endpoint and have the ingestion application read from the topic.

B : Write data to a Cloud Storage bucket instead of a REST API endpoint and have the ingestion application read from the bucket.

C : Write data to a Cloud SQL Postgres database endpoint and have the ingestion application query the database.

D : Create a Hadoop cluster in Compute Engine using managed instance groups and write data to an Hbase database and have the application query the database.

Answer: A

Question 4

Analysts are using Cloud Data Studio for analyzing data sets. They would like to improve the performance of the time required to update tables and charts when working with the data. What would you recommend they try to improve performance?

A : Use an imported data source

B : Use a blended data source

C : Use a live data source

D : Use an extracted data source

Answer: D

Question 5

Your startup has a web application that currently serves customers out of a single region in Asia. You are

targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for

cost, and your post-funding goat is to optimize for global presence and performance. You must use a native

JDBC driver. What should you do?

A : Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oudSpanner instances after securing funding.

B : Use a Cloud SQL for PostgreSQL highly available instance first, and 8»gtable with US. Europe, andAsia replication alter securing funding

C : Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia aftersecuring funding.

D : Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highlyavailable configuration after securing funding.

Answer: A

Page: 1 / 95

Total 472 Questions | Updated On: Nov 19, 2024

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