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You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

A.

Disable caching by editing the report settings.

B.

Disable caching in BigQuery by editing table details.

C.

Refresh your browser tab showing the visualizations.

D.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

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 sliding time window with a duration of 60 minutes.

C.

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

D.

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

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

A.

Linear regression

B.

Logistic classification

C.

Recurrent neural network

D.

Feedforward neural network

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A.

There are very few occurrences of mutations relative to normal samples.

B.

There are roughly equal occurrences of both normal and mutated samples in the database.

C.

You expect future mutations to have different features from the mutated samples in the database.

D.

You expect future mutations to have similar features to the mutated samples in the database.

E.

You already have labels for which samples are mutated and which are normal in the database.

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

A.

Include ORDER BY DESK on timestamp column and LIMIT to 1.

B.

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

C.

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

D.

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

A.

Issue a command to restart the database servers.

B.

Retry the query with exponential backoff, up to a cap of 15 minutes.

C.

Retry the query every second until it comes back online to minimize staleness of data.

D.

Reduce the query frequency to once every hour until the database comes back online.

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy?

A.

Grant the consultant the Viewer role on the project.

B.

Grant the consultant the Cloud Dataflow Developer role on the project.

C.

Create a service account and allow the consultant to log on with it.

D.

Create an anonymized sample of the data for the consultant to work with in a different project.

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

A.

Update the current pipeline and use the drain flag.

B.

Update the current pipeline and provide the transform mapping JSON object.

C.

Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

D.

Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

A.

Supervised learning to determine which transactions are most likely to be fraudulent.

B.

Unsupervised learning to determine which transactions are most likely to be fraudulent.

C.

Clustering to divide the transactions into N categories based on feature similarity.

D.

Supervised learning to predict the location of a transaction.

E.

Reinforcement learning to predict the location of a transaction.

F.

Unsupervised learning to predict the location of a transaction.

Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

A.

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.2. Load the booking and user profile data into a BigQuery table.

B.

1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.2. Load the booking and user profile data into a BigQuery table.

C.

1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2. Create a policy tag with the email mask as the data masking rule.3. Assign the policy to the email field in both tables. A4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.

D.

1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.2. Create a policy tag with the default masking value as the data masking rule.3. Assign the policy to the email field in both tables.4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts