Sep-2021 Latest SureTorrent Professional-Data-Engineer Exam Dumps with PDF and Exam Engine Free Updated Today!
Following are some new Professional-Data-Engineer Real Exam Questions!
NEW QUESTION 28
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?
- A. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
- B. Try to collect more data and increase the size of your dataset.
- C. Increase the share of the test sample in the train-test split.
- D. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
Answer: A
NEW QUESTION 29
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
- A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
- B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
- C. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
Answer: D
NEW QUESTION 30
Which of these is not a supported method of putting data into a partitioned table?
- A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- B. Create a partitioned table and stream new records to it every day.
- C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
"Partitioned". - D. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
Answer: C
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION 31
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/ Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket.
The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?
- A. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
- B. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
- C. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
- D. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
Answer: C
Explanation:
Increase in number of undelivered messages shows that the messages are not getting subscribed.
NEW QUESTION 32
You need to choose a database for a new project that has the following requirements:
* Fully managed
* Able to automatically scale up
* Transactionally consistent
* Able to scale up to 6 TB
* Able to be queried using SQL
Which database do you choose?
- A. Cloud Datastore
- B. Cloud Spanner
- C. Cloud Bigtable
- D. Cloud SQL
Answer: B
Explanation:
https://cloud.google.com/products/databases
NEW QUESTION 33
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
* You will batch-load the posts once per day and run them through the Cloud Natural Language API.
* You will extract topics and sentiment from the posts.
* You must store the raw posts for archiving and reprocessing.
* You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?
- A. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
- B. Store the social media posts and the data extracted from the API in BigQuery.
- C. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
- D. Store the social media posts and the data extracted from the API in Cloud SQL.
Answer: C
NEW QUESTION 34
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. Segregate data across multiple tables or databases.
- B. Ensure that the data is encrypted at all times.
- C. Restrict access to tables by role.
- D. Disable writes to certain tables.
- E. Use Google Stackdriver Audit Logging to determine policy violations.
- F. Restrict BigQuery API access to approved users.
Answer: C,E,F
NEW QUESTION 35
What is the HBase Shell for Cloud Bigtable?
- A. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.
- B. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
- C. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
- D. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
Answer: B
Explanation:
Explanation
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell
NEW QUESTION 36
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.
You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
- A. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
- B. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
- C. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
- D. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
Answer: A
NEW QUESTION 37
Your analytics team wants to build a simple statistical model to determine which customers are most likely
to work with your company again, based on a few different metrics. They want to run the model on Apache
Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud
Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on
a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.
How should you optimize the cluster for cost?
- A. Migrate the workload to Google Cloud Dataflow
- B. Use pre-emptible virtual machines (VMs) for the cluster
- C. Use a higher-memory node so that the job runs faster
- D. Use SSDs on the worker nodes so that the job can run faster
Answer: A
NEW QUESTION 38
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. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
- B. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
- C. Re-write the application to load accumulated data every 2 minutes.
- D. Convert the streaming insert code to batch load for individual messages.
Answer: C
NEW QUESTION 39
Which of these is NOT a way to customize the software on Dataproc cluster instances?
- A. Set initialization actions
- B. Log into the master node and make changes from there
- C. Modify configuration files using cluster properties
- D. Configure the cluster using Cloud Deployment Manager
Answer: D
Explanation:
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 40
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?
- A. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
Answer: C
NEW QUESTION 41
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
- A. Add capacity (memory and disk space) to the database server by the order of 200.
- B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
- C. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
- D. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
Answer: C
NEW QUESTION 42
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
- A. 'bigquery-public-data.noaa_gsod.gsod*`
- B. 'bigquery-public-data.noaa_gsod.gsod'
- C. 'bigquery-public-data.noaa_gsod.gsod'*
- D. bigquery-public-data.noaa_gsod.gsod*
Answer: D
NEW QUESTION 43
Case Study 2 - MJTelco
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- B. Create a table called tracking_table and include a DATE column.
- C. Create a table called tracking_table with a TIMESTAMP column to represent the day.
- D. Create a partitioned table called tracking_table and include a TIMESTAMP column.
Answer: D
NEW QUESTION 44
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
- A. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
- B. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
- C. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
- D. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
Answer: D
Explanation:
As time is less, use cloud NLP and entity is used to label general subjects, sentiment label for sentiment analysis.
NEW QUESTION 45
You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?
- A. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences. - B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the
models using Cloud Dataproc. Call the models from your application. - C. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application. - D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate
preferences.
Answer: A
NEW QUESTION 46
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?
C: Set the following in your entity options: exclude_from_indexes = 'actors, tags' D: Set the following in your entity options: exclude_from_indexes = 'date_published'
- A. Option A
- B. Option D
- C. Option C
- D. Option B.
Answer: A
NEW QUESTION 47
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
- A. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
- B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
- C. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application. - D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
Answer: D
NEW QUESTION 48
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