PDF Download Free of Professional-Data-Engineer Valid Practice Test Questions [Q109-Q129]

NO.109 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?

Disable caching by editing the report settings.

Disable caching in BigQuery by editing table details.

Refresh your browser tab showing the visualizations.

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

NO.110 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 dat
a. Which three machine learning applications can you use? (Choose three.)

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

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

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

Supervised learning to predict the location of a transaction.

Reinforcement learning to predict the location of a transaction.

Unsupervised learning to predict the location of a transaction.

NO.111 Scaling a Cloud Dataproc cluster typically involves ____.

increasing or decreasing the number of worker nodes

increasing or decreasing the number of master nodes

moving memory to run more applications on a single node

deleting applications from unused nodes periodically

NO.112 You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.
Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

Convert all daily log tables into date-partitioned tables

Convert the sharded tables into a single partitioned table

Enable query caching so you can cache data from previous months

Create separate views to cover each month, and query from these views

NO.113 Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Threading

Serialization

Dropout Methods

Dimensionality Reduction

NO.114 An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want
to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-
level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for
other projects is assigned to those projects. What should they do?

Create and share an authorized view that provides the aggregate results.

Create and share a new dataset and view that provides the aggregate results.

Create and share a new dataset and table that contains the aggregate results.

Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

NO.115 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_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Option A

Option B.

Option C

Option D

NO.116 Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

Perform hyperparameter tuning

Train a classifier with deep neural networks, because neural networks would always beat SVMs

Deploy the model and measure the real-world AUC; it’s always higher because of generalization

Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

NO.117 A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time.
This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Implement clustering in BigQuery on the ingest date column.

Implement clustering in BigQuery on the package-tracking ID column.

Tier older data onto Cloud Storage files, and leverage extended tables.

Re-create the table using data partitioning on the package delivery date.

NO.118 You have an Apache Kafka cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read from PubSub and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read from PubSub and write to GCS.

NO.119 You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

Add a SideInput that returns a Boolean if the element is corrupt.

Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

NO.120 Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

Use Google Stackdriver Audit Logs to review data access.

Get the identity and access management IIAM) policy of each table

Use Stackdriver Monitoring to see the usage of BigQuery query slots.

Use the Google Cloud Billing API to see what account the warehouse is being billed to.

NO.121 You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

Use bq loadto load a batch of sensor data every 60 seconds.

Use a Cloud Dataflow pipeline to stream data into the BigQuery table.

Use the INSERT statement to insert a batch of data every 60 seconds.

Use the MERGE statement to apply updates in batch every 60 seconds.

NO.122 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

NO.123 Which of the following are feature engineering techniques? (Select 2 answers)

Hidden feature layers

Feature prioritization

Crossed feature columns

Bucketization of a continuous feature

NO.124 You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour. How should you design the solution?

Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

NO.125 You’re training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you’ve discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you’d like to engineer a feature that incorporates this physical dependency.
What should you do?

Provide latitude and longitude as input vectors to your neural net.

Create a numeric column from a feature cross of latitude and longitude.

Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.

Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.

NO.126 MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than
50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments – development/test, staging, and production – to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure.
We also need environments in which our data scientists can carefully study and quickly adapt our models.
Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud’s machine learning will allow our quantitative researchers to work on our high- value problems instead of problems with our data pipelines.
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Rowkey: date#device_id
Column data: data_point

Rowkey: date
Column data: device_id,data_point

Rowkey: device_id
Column data: date, data_point

Rowkey: data_point
Column data: device_id,date

Rowkey: date#data_point
Column data: device_id

NO.127 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.)

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

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

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

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

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

NO.128 Which of the following is NOT true about Dataflow pipelines?

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

Dataflow pipelines can consume data from other Google Cloud services

Dataflow pipelines can be programmed in Java

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

NO.129 When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

500 TB

1 GB

1 TB

500 GB

PDF Download Free of Professional-Data-Engineer Valid Practice Test Questions [Q109-Q129]

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