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Google Professional-Data-Engineer - Google Professional Data Engineer Exam

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Total 400 questions

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

A.

Load data into different partitions.

B.

Load data into a different dataset for each client.

C.

Put each client’s BigQuery dataset into a different table.

D.

Restrict a client’s dataset to approved users.

E.

Only allow a service account to access the datasets.

F.

Use the appropriate identity and access management (IAM) roles for each client’s users.

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.

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?

A.

Specify the TableReference object in the code.

B.

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

C.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

D.

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

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

A.

Re-write the application to load accumulated data every 2 minutes.

B.

Convert the streaming insert code to batch load for individual messages.

C.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

D.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

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.

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?

A.

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

B.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

C.

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

D.

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

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

A.

Continuously retrain the model on just the new data.

B.

Continuously retrain the model on a combination of existing data and the new data.

C.

Train on the existing data while using the new data as your test set.

D.

Train on the new data while using the existing data as your test set.

You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.

Tom,555 X street

Tim,553 Y street

Sam, 111 Z street

Which operation is best suited for the above data processing requirement?

A.

ParDo

B.

Sink API

C.

Source API

D.

Data extraction

Which of the following is NOT true about Dataflow pipelines?

A.

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

B.

Dataflow pipelines can consume data from other Google Cloud services

C.

Dataflow pipelines can be programmed in Java

D.

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

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.

Run a local version of Jupiter on the laptop.

B.

Grant the user access to Google Cloud Shell.

C.

Host a visualization tool on a VM on Google Compute Engine.

D.

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.