What Printable Professional-Data-Engineer Actual Test Is

NEW QUESTION 1

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

• A. Export the data into a Google Sheet for virtualization.
• B. Create an additional table with only the necessary columns.
• C. Create a view on the table to present to the virtualization tool.
• D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Answer: C

NEW QUESTION 2

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

• A. Store the common data in BigQuery as partitioned tables.
• B. Store the common data in BigQuery and expose authorized views.
• C. Store the common data encoded as Avro in Google Cloud Storage.
• D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Answer: B

NEW QUESTION 3

Which software libraries are supported by Cloud Machine Learning Engine?

• A. Theano and TensorFlow
• B. Theano and Torch
• C. TensorFlow
• D. TensorFlow and Torch

Answer: C

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does

NEW QUESTION 4

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

• A. Do not use a production instance.
• B. Run your test for at least 10 minutes.
• C. Before you test, run a heavy pre-test for several minutes.
• D. Use at least 300 GB of data.

Answer: A

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you
plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION 5

Cloud Dataproc charges you only for what you really use with billing.

• A. month-by-month
• B. minute-by-minute
• C. week-by-week
• D. hour-by-hour

Answer: B

Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.
Reference: https://cloud.google.com/dataproc/docs/concepts/overview

NEW QUESTION 6

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.

Answer: C

NEW QUESTION 7

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

• A. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
• B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
• C. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
• D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

Answer: B

NEW QUESTION 8

Which of these are examples of a value in a sparse vector? (Select 2 answers.)

• A. [0, 5, 0, 0, 0, 0]
• B. [0, 0, 0, 1, 0, 0, 1]
• C. [0, 1]
• D. [1, 0, 0, 0, 0, 0, 0]

Answer: CD

Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1. [0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s. Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

NEW QUESTION 9

You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

• A. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
• B. Place the MariaDB instances in an Instance Group with a Health Check.
• C. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
• D. Install the StackDriver Agent and configure the MySQL plugin.

Answer: C

NEW QUESTION 10

You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

• A. cron
• B. Cloud Composer
• C. Cloud Scheduler
• D. Workflow Templates on Cloud Dataproc

Answer: D

NEW QUESTION 11

Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

• A. Dataproc Worker
• B. Dataproc Viewer
• C. Dataproc Runner
• D. Dataproc Editor

Answer: A

Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION 12

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A. Deploy a Cloud Dataproc cluste
• B. Use a standard persistent disk and 50% preemptible worker
• C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• D. Deploy a Cloud Dataproc cluste
• E. Use an SSD persistent disk and 50% preemptible worker
• F. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• G. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
• H. Install the Cloud Storage connector, and store the data in Cloud Storag
• I. Change references in scripts from hdfs:// to gs://
• J. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
• K. Change references in scripts from hdfs:// to gs://

Answer: A

NEW QUESTION 13

Which of these statements about BigQuery caching is true?

• A. By default, a query's results are not cached.
• B. BigQuery caches query results for 48 hours.
• C. Query results are cached even if you specify a destination table.
• D. There is no charge for a query that retrieves its results from cache.

Answer: D

Explanation:
When query results are retrieved from a cached results table, you are not charged for the query. BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table. Reference: https://cloud.google.com/bigquery/querying-data#query-caching

NEW QUESTION 14

Why do you need to split a machine learning dataset into training data and test data?

• A. So you can try two different sets of features
• B. To make sure your model is generalized for more than just the training data
• C. To allow you to create unit tests in your code
• D. So you can use one dataset for a wide model and one for a deep model

Answer: B

Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

NEW QUESTION 15

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

• A. The CSV data loaded in BigQuery is not flagged as CSV.
• B. The CSV data has invalid rows that were skipped on import.
• C. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
• D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer: B

NEW QUESTION 16

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.

Answer: BCE

NEW QUESTION 17

You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the
Google-recommended cloud native architecture for this scenario?

• A. Edge TPUs as sensor devices for storing and transmitting the messages.
• B. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
• C. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
• D. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

Answer: C

NEW QUESTION 18

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?

• A. Threading
• B. Serialization
• C. Dropout Methods
• D. Dimensionality Reduction

Answer: C

Explanation:
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505

NEW QUESTION 19

You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? Choose 2 answers.

• A. Review Key Visualizer metric
• B. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
• C. Review Key Visualizer metric
• D. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
• E. Monitor the latency of write operation
• F. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
• G. Monitor storage utilizatio
• H. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
• I. Monitor latency of read operation
• J. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

Answer: AC

NEW QUESTION 20

Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

• A. Create a file on a shared file and have the application servers write all bid events to that fil
• B. Process the file with Apache Hadoop to identify which user bid first.
• C. Have each application server write the bid events to Cloud Pub/Sub as they occu
• D. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
• E. Set up a MySQL database for each application server to write bid events int
• F. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
• G. Have each application server write the bid events to Google Cloud Pub/Sub as they occu
• H. Use a pull subscription to pull the bid events using Google Cloud Dataflo
• I. Give the bid for each item to the userIn the bid event that is processed first.

Answer: C

NEW QUESTION 21

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

• A. Disable writes to certain tables.
• B. Restrict access to tables by role.
• C. Ensure that the data is encrypted at all times.
• D. Restrict BigQuery API access to approved users.
• E. Segregate data across multiple tables or databases.
• F. Use Google Stackdriver Audit Logging to determine policy violations.

Answer: BDF

NEW QUESTION 22
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