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NEW QUESTION 93
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
- A. Use the LIMIT keyword to reduce the number of rows returned.
- B. Use the bq query - -maximum_bytes_billedflag to restrict the number of bytes billed.
- C. Recreate the table with a partitioning column and clustering column.
- D. Create a separate table for each ID.
Answer: A
Explanation:
Explanation
NEW QUESTION 94
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. 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.
- C. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
- D. 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.
Answer: A
NEW QUESTION 95
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?
- A. Windows at every 100 MB of data
- B. Single, Global Window
- C. Windows at every 1 minute
- D. Windows at every 10 minutes
Answer: B
Explanation:
Explanation
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection
NEW QUESTION 96
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- C. Load the data every 30 minutes into a new partitioned table in BigQuery.
- D. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
Answer: D
Explanation:
Explanation
NEW QUESTION 97
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
- A. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
- B. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
- C. Use Cloud ML Engine for training existing Spark ML models
- D. Rewrite your models on TensorFlow, and start using Cloud ML Engine
Answer: B
Explanation:
https://cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml
NEW QUESTION 98
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
- A. Cloud Bigtable
- B. Cloud SQL
- C. Cloud Datastore
- D. BigQuery
Answer: C
Explanation:
https://cloud.google.com/datastore/docs/concepts/overview
NEW QUESTION 99
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.
- A. zone
- B. label
- C. type
- D. node
Answer: A
Explanation:
Explanation
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.
Reference:
https://cloud.google.com/dataproc/docs/tutorials/python-library-example#create_a_new_cloud_dataproc_cluste
NEW QUESTION 100
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. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
- 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. Stay on CPUs, and increase the size of the cluster you're training your model on.
Answer: A
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 101
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?
- A. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
- B. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
- C. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
- D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
Answer: C
Explanation:
Cloud composer is used to schedule the interdependent jobs.
NEW QUESTION 102
What are two of the benefits of using denormalized data structures in BigQuery?
- A. Reduces the amount of data processed, increases query speed
- B. Reduces the amount of data processed, reduces the amount of storage required
- C. Reduces the amount of storage required, increases query speed
- D. Increases query speed, makes queries simpler
Answer: D
Explanation:
Explanation
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION 103
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
- A. [0, 1]
- B. [0, 0, 0, 1, 0, 0, 1]
- C. [0, 5, 0, 0, 0, 0]
- D. [1, 0, 0, 0, 0, 0, 0]
Answer: A,D
Explanation:
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 104
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)
- A. Preserve the structure of the data as much as possible.
- B. Use BigQuery UPDATE to further reduce the size of the dataset.
- C. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
- D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- E. Denormalize the data as must as possible.
Answer: C,D
NEW QUESTION 105
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat A.
Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
- A. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
- B. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
- C. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
- D. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
Answer: A
NEW QUESTION 106
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. Run your test for at least 10 minutes.
- B. Before you test, run a heavy pre-test for several minutes.
- C. Use at least 300 GB of data.
- D. Do not use a production instance.
Answer: D
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.
NEW QUESTION 107
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
- A. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
- B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
- C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
- D. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
Answer: A
NEW QUESTION 108
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?
- A. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
- B. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
- C. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
- D. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
Answer: A
NEW QUESTION 109
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 social media posts and the data extracted from the API in Cloud SQL.
- B. Store the social media posts and the data extracted from the API in BigQuery.
- C. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
- D. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
Answer: C
Explanation:
Social media posts can images/videos which cannot be stored in bigquery/
NEW QUESTION 110
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Exam Topics
The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:
Topic 1. Designing Data Processing Systems
To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.
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