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Viewing questions 71-80 out of questions

Questions # 71:

Your company's customer_order table in BigOuery stores the order history for 10 million customers, with a table size of 10 PB. You need to create a dashboard for the support team to view the order history. The dashboard has two filters, countryname and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results. However, the dashboard is slow to show the results when applying the filters to the following query:

Question # 71

How should you redesign the BigQuery table to support faster access?

Options:

Cluster the table by country field, and partition by username field.

Partition the table by country and username fields.

Cluster the table by country and username fields

Partition the table by _PARTITIONTIME.

Questions # 72:

You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

Options:

BigQuery

Cloud Bigtable

Cloud Datastore

Cloud SQL for PostgreSQL

Questions # 73:

You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

Options:

Give analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

Give analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.

Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset

Give analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

Questions # 74:

You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

Options:

When creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery"s ML. EVALUATE clause without specifying any transformations on the raw input data.

When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE

Use a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.

Preprocess all data using Dataflow. At prediction time, use BigOuery"s ML. EVALUATE clause without specifying any further transformations on the input data.

Questions # 75:

Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:

• The old data can be deleted anytime

• You plan to use the visualization layer for current and historical reporting

• The old data should be available instantly when accessed

• There should not be any charges for data retrieval.

What should you do to optimize for cost?

Options:

Create the bucket with the Autoclass storage class feature.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline. and 365 days to archive storage class. Delete old data as needed.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline. and 365 days to archive storage class Delete old data as needed.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearlme. 45 days to coldline. and 60 days to archive storage class Delete old data as needed.

Questions # 76:

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Options:

Use federated data sources, and check data in the SQL query.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

Questions # 77:

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?

Options:

Specify the TableReference object in the code.

Use .fromQuery operation to read specific fields from the table.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Questions # 78:

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?

Options:

Use a fixed-time window with a duration of 60 minutes.

Use a sliding time window with a duration of 60 minutes.

Use a session window with a gap time duration of 60 minutes.

Use a global window with a time based trigger with a delay of 60 minutes.

Questions # 79:

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?

Options:

Cloud SQL

BigQuery

Cloud Bigtable

Cloud Datastore

Questions # 80:

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?