Pass Authentic Google Professional-Data-Engineer with Free Practice Tests and Exam Dumps [Q63-Q86]

Pass Authentic Google Professional-Data-Engineer with Free Practice Tests and Exam Dumps

New Professional-Data-Engineer  Exam Questions Real Google Dumps

The Google Professional-Data-Engineer exam is designed for professionals who have experience working with data processing systems in a business environment. Candidates should have a thorough understanding of data modeling, data warehousing, and data transformation. Additionally, they should have experience working with cloud-based data storage and processing systems such as Google Cloud Platform, AWS, or Microsoft Azure.

 

NEW QUESTION # 63
Which Java SDK class can you use to run your Dataflow programs locally?

• A. LocalRunner
• B. LocalPipelineRunner
• C. DirectPipelineRunner
• D. MachineRunner

Answer: C

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners
/DirectPipelineRunner

NEW QUESTION # 64
You are building a model to make clothing recommendations. You know a user's fashion pis 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. Train on the existing data while using the new data as your test set.
• B. Train on the new data while using the existing data as your test set.
• C. Continuously retrain the model on just the new data.
• D. Continuously retrain the model on a combination of existing data and the new data.

Answer: B

NEW QUESTION # 65
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

• A. Use Cloud Vision API by providing custom labels as recognition hints.
• B. Train your own image recognition model leveraging transfer learning techniques.
• C. Use Cloud Vision AutoML with the existing dataset.
• D. Use Cloud Vision AutoML, but reduce your dataset twice.

Answer: C

NEW QUESTION # 66
You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

• A. Store and process the entire dataset in BigQuery.
• B. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
• C. Store and process the entire dataset in Cloud Bigtable.
• D. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.

Answer: B

NEW QUESTION # 67
You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

• A. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.
• B. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
• C. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
• D. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

Answer: C

NEW QUESTION # 68
What are the minimum permissions needed for a service account used with Google Dataproc?

• A. Read and write to Google Cloud Storage; write to Google Cloud Logging
• B. Write to Google Cloud Storage; read to Google Cloud Logging
• C. Execute to Google Cloud Storage; write to Google Cloud Logging
• D. Execute to Google Cloud Storage; execute to Google Cloud Logging

Answer: A

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.

NEW QUESTION # 69
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's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

• A. The disk size per worker
• B. The maximum number of workers
• C. The zone
• D. The number of workers

Answer: C

NEW QUESTION # 70
How can you get a neural network to learn about relationships between categories in a categorical feature?

• A. Create a hash bucket
• B. Create a one-hot column
• C. Create a multi-hot column
• D. Create an embedding column

Answer: D

Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it.
But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html

NEW QUESTION # 71
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems.
Which solutions should you choose?

• A. Cloud Speech-to-Text API
• B. Cloud Natural Language API
• C. Cloud AutoML Natural Language
• D. Dialogflow Enterprise Edition

Answer: C

NEW QUESTION # 72
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

• A. SELECT
• B. BETWEEN
• C. WHERE
• D. LIMIT

Answer: A

Explanation:
SELECT allows you to query specific columns rather than the whole table. LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference: https://cloud.google.com/bigquery/launch-
checklist#architecture_design_and_development_checklist

NEW QUESTION # 73
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?

• A. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
• B. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
• C. Convert your batch BQ queries into interactive BQ queries.
• D. Create an additional project to overcome the 2K on-demand per-project quota.

Answer: B

Explanation:
Explanation/Reference:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

NEW QUESTION # 74
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

• A. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
• B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
• C. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
• D. Create groups for your users and give those groups access to the dataset

Answer: C

Explanation:
Explanation

NEW QUESTION # 75
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?

• A. Classifier
• B. Clustering estimator
• C. Unsupervised learning
• D. Regressor

Answer: D

Explanation:
Explanation
Regression is the supervised learning task for modeling and predicting continuous, numeric variables.
Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms

NEW QUESTION # 76
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.

• A. row key
• B. master key
• C. unique key
• D. primary key

Answer: A

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 77
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. Serialization
• B. Dropout Methods
• C. Threading
• D. Dimensionality Reduction

Answer: B

Explanation:
Explanation/Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877

NEW QUESTION # 78
You are designing a cloud-native historical data processing system to meet the following conditions:
The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis

tools including Cloud Dataproc, BigQuery, and Compute Engine.
A streaming data pipeline stores new data daily.

Peformance is not a factor in the solution.

The solution design should maximize availability.

How should you design data storage for this solution?

• A. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
• B. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
• C. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
• D. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer: D

NEW QUESTION # 79
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

• A. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
• B. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
• C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
• D. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: B

NEW QUESTION # 80
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?

• A. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
• B. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
• C. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
• D. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

Answer: A

Explanation:
Explanation

NEW QUESTION # 81
You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

• A. Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
• B. Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
• C. Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.
• D. Create a new TSV file for the remaining files by generating signed URLs with a longer validity period.Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

Answer: D

Explanation:
A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete. This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. References:
* Signed URLs | Cloud Storage Documentation
* V4 signing process with Cloud Storage tools
* V4 signing process withyour own program
* Using a URL list file
* What Is a 403 Forbidden Error (and How Can I Fix It)?

NEW QUESTION # 82
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

• A. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
• B. Use the NOW () function in BigQuery to record the event's time.
• C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
• D. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Answer: A

Explanation:
Topic 2, 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.

NEW QUESTION # 83
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

• A. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP type. Reload the data.
• B. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now on.
• C. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
• D. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
  Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
• E. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause ensuring that the value of IS_NEWmust be true.

Answer: E

NEW QUESTION # 84
Which of these is NOT a way to customize the software on Dataproc cluster instances?

• A. Set initialization actions
• B. Modify configuration files using cluster properties
• C. Configure the cluster using Cloud Deployment Manager
• D. Log into the master node and make changes from there

Answer: C

Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.
When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties

NEW QUESTION # 85
Which of the following are examples of hyperparameters? (Select 2 answers.)

• A. Biases
• B. Weights
• C. Number of hidden layers
• D. Number of nodes in each hidden layer

Answer: C,D

Explanation:
Explanation
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview

NEW QUESTION # 86
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