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NEW QUESTION 76
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: B
Explanation:
https://cloud.google.com/bigquery/docs/monitoring
NEW QUESTION 77
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 on each message in the Cloud Pub/Sub subscriber application as they are received.
- B. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
- C. Use the NOW () function in BigQuery to record the event's time.
- D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
Answer: B
NEW QUESTION 78
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?
- A. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
- B. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
- C. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
- D. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
Answer: A
Explanation:
https://cloud.google.com/sql/docs/mysql/high-availability
NEW QUESTION 79
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?
- A. Cloud BigTable
- B. Cloud Datastore
- C. Cloud SQL
- D. BigQuery
Answer: A
Explanation:
Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/
NEW QUESTION 80
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 81
You are analyzing the price of a company's stock. Every 5 seconds, you need to compute a moving average of the past 30 seconds' worth of data. You are reading data from Pub/Sub and using DataFlow to conduct the analysis. How should you set up your windowed pipeline?
- A. Use a sliding window with a duration of 30 seconds and a period of 5 seconds. Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow()
- B. Use a sliding window with a duration of 5 seconds. Emit results by setting the following trigger:
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(30)) - C. Use a fixed window with a duration of 30 seconds. Emit results by setting the following trigger:
AfterWatermark.pastEndOfWindow().plusDelayOf(Duration.standardSeconds(5)) - D. Use a fixed window with a duration of 5 seconds. Emit results by setting the following trigger:
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(30))
Answer: C
NEW QUESTION 82
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?
- A. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
- B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
- C. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.
- D. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
Answer: C
NEW QUESTION 83
Which action can a Cloud Dataproc Viewer perform?
- A. List the jobs.
- B. Delete a cluster.
- C. Create a cluster.
- D. Submit a job.
Answer: A
Explanation:
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
Reference:
https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_operations
_summary
NEW QUESTION 84
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
Explanation:
Explanation/Reference:
NEW QUESTION 85
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
- A. [0, 1]
- B. [0, 5, 0, 0, 0, 0]
- C. [0, 0, 0, 1, 0, 0, 1]
- D. [1, 0, 0, 0, 0, 0, 0]
Answer: A,D
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
0 s and 1s.
Reference:
https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations
NEW QUESTION 86
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?
- A. Disable caching by editing the report settings.
- B. Refresh your browser tab showing the visualizations.
- C. Disable caching in BigQuery by editing table details.
- D. Clear your browser history for the past hour then reload the tab showing the virtualizations.
Answer: A
Explanation:
Explanation/Reference:
Reference: https://support.google.com/datastudio/answer/7020039?hl=en
NEW QUESTION 87
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?
- A. Stay on CPUs, and increase the size of the cluster you're training your model on.
- B. Use Cloud TPUs without any additional adjustment to your code.
- C. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
- D. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
Answer: D
Explanation:
Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.
NEW QUESTION 88
You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?
- A. Change the processing job to use Google Cloud Dataproc instead.
- B. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
- C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
- D. Manually start the Cloud Dataflow job each morning when you get into the office.
Answer: C
NEW QUESTION 89
You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture
anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS
endpoint that you have created to take action of these anomalous events as they occur. Your custom
HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause
of these duplicate messages?
- A. Your custom endpoint is not acknowledging messages within the acknowledgement deadline.
- B. The Cloud Pub/Sub topic has too many messages published to it.
- C. Your custom endpoint has an out-of-date SSL certificate.
- D. The message body for the sensor event is too large.
Answer: C
NEW QUESTION 90
You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes.
You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?
- A. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
- B. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
- C. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
- D. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and profile for the analytics workload.
Answer: A
NEW QUESTION 91
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?
- A. Weights
- B. Biases
- C. Continuous features
- D. Input values
Answer: A,B
Explanation:
Explanation
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference:
https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground
NEW QUESTION 92
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
* The user profile: What the user likes and doesn't like to eat
* The user account information: Name, address, preferred meal times
* The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?
- A. Cloud Datastore
- B. BigQuery
- C. Cloud Bigtable
- D. Cloud SQL
Answer: B
NEW QUESTION 93
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
- A. An hourly watermark
- B. An event time trigger
- C. A processing time trigger
- D. The with Allowed Lateness method
Answer: C
Explanation:
Explanation
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline.
Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers
NEW QUESTION 94
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
- C. Create a cron schedule in Cloud Dataprep.
- D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
Answer: B
Explanation:
Explanation
NEW QUESTION 95
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.
- A. before
- B. once
- C. after
- D. only if
Answer: A
Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION 96
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?
- A. Manually configure the index in your index config as follows:
- B. Set the following in your entity options: exclude_from_indexes = 'actors, tags'
- C. Set the following in your entity options: exclude_from_indexes = 'date_published'
- D. Manually configure the index in your index config as follows:
Answer: A
NEW QUESTION 97
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 98
You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single
resource-constrained virtual machine. Which learning algorithm should you use?
- A. Logistic classification
- B. Linear regression
- C. Recurrent neural network
- D. Feedforward neural network
Answer: B
NEW QUESTION 99
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
- A. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
- B. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- C. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
- D. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
Answer: B
NEW QUESTION 100
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