Professional-Data-Engineer Real Questions

QUESTION 95
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

QUESTION 96
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don’t get slots to execute their query and you need to correct this. You’d like to avoid introducing new projects to your account.
What should you do?

Convert your batch BQ queries into interactive BQ queries.

Create an additional project to overcome the 2K on-demand per-project quota.

Switch to flat-rate pricing and establish a hierarchical priority model for your projects.

Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

QUESTION 97
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of
which can take on multiple values. For example, in the entity ‘Movie’the property ‘actors’and the
property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query
would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with
tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the
number of indexes?

Manually configure the index in your index config as follows:

Set the following in your entity options: exclude_from_indexes = ‘actors, tags’

Set the following in your entity options: exclude_from_indexes = ‘date_published’

QUESTION 98
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

1 continuous and 2 categorical

3 categorical

3 continuous

2 continuous and 1 categorical

QUESTION 99
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

Use Cloud Deployment Manager to automate access provision.

Introduce resource hierarchy to leverage access control policy inheritance.

Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

QUESTION 100
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

Use K-means Clustering to detect faces in the pixels.

Use feature engineering to add features for eyes, noses, and mouths to the input data.

Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

QUESTION 101
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

QUESTION 102
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?

Build materialized views on top of the sales table to aggregate data at the day and month level.

Build authorized views on top of the sales table to aggregate data at the day and month level.

Enable Bl Engine and add your sales table as a preferred table.

Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.

QUESTION 103
Dataproc clusters contain many configuration files. To update these files, you will need to use the –properties option. The format for the option is: file_prefix:property=_____.

details

value

null

id

QUESTION 104
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.

Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

QUESTION 105
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Include multiple time series values within the row key

Keep the row keep as an 8 bit integer

Keep your row key reasonably short

Keep your row key as long as the field permits

QUESTION 106
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

QUESTION 107
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

Unsupervised learning

Regressor

Classifier

Clustering estimator

QUESTION 108
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. What should you do?

Use Analytics Hub to control data access, and provide third party companies with access to the dataset

Create a Dataflow job that reads the data in frequent time intervals and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

QUESTION 109
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)

Publisher throughput quota is too small.

Total outstanding messages exceed the 10-MB maximum.

Error handling in the subscriber code is not handling run-time errors properly.

The subscriber code cannot keep up with the messages.

The subscriber code does not acknowledge the messages that it pulls.

QUESTION 110
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl.
being able to reprocess all failing data).
What should you do?

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

Add a try… catch block to your DoFn that transforms the data, extract erroneous rows from logs.

Add a try… catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a try… catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

QUESTION 111
You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

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