Top Tips Of Regenerate Professional-Data-Engineer Dumps

NEW QUESTION 1

Which of the following is NOT one of the three main types of triggers that Dataflow supports?

• A. Trigger based on element size in bytes
• B. Trigger that is a combination of other triggers
• C. Trigger based on element count
• D. Trigger based on time

Answer: A

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way
Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION 2

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

• A. Issue a command to restart the database servers.
• B. Retry the query with exponential backoff, up to a cap of 15 minutes.
• C. Retry the query every second until it comes back online to minimize staleness of data.
• D. Reduce the query frequency to once every hour until the database comes back online.

Answer: B

Explanation:
https://cloud.google.com/sql/docs/mysql/manage-connections#backoff

NEW QUESTION 3

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

• A. There are very few occurrences of mutations relative to normal samples.
• B. There are roughly equal occurrences of both normal and mutated samples in the database.
• C. You expect future mutations to have different features from the mutated samples in the database.
• D. You expect future mutations to have similar features to the mutated samples in the database.
• E. You already have labels for which samples are mutated and which are normal in the database.

Answer: AD

Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection

NEW QUESTION 4

You need to compose visualizations for operations teams with the following requirements: Which approach meets the requirements?

• A. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
• B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
• C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
• D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Answer: C

NEW QUESTION 5

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

Answer: C

NEW QUESTION 6

What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

• A. Sessions
• B. OutputCriteria
• C. Windows
• D. Triggers

Answer: D

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

NEW QUESTION 7

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. Manually start the Cloud Dataflow job each morning when you get into the office.
• C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
• D. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Answer: C

NEW QUESTION 8

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

Answer: C

Explanation:
https://cloud.google.com/automl-tables/docs/prepare

NEW QUESTION 9

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

• A. Use bq load to load a batch of sensor data every 60 seconds.
• B. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
• C. Use the INSERT statement to insert a batch of data every 60 seconds.
• D. Use the MERGE statement to apply updates in batch every 60 seconds.

Answer: C

NEW QUESTION 10

Which of the following is not possible using primitive roles?

• A. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
• B. Give UserA owner access and UserB editor access for all datasets in a project.
• C. Give a user access to view all datasets in a project, but not run queries on them.
• D. Give GroupA owner access and GroupB editor access for all datasets in a project.

Answer: C

Explanation:
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles

NEW QUESTION 11

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

• A. Send the data to Google Cloud Datastore and then export to BigQuery.
• B. Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
• C. Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
• D. Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Answer: B

NEW QUESTION 12

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

Answer: C

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

NEW QUESTION 13

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

• A. Use Cloud Dataprep to find null values in sample source dat
• B. Convert all nulls to ‘none’ using a Cloud Dataproc job.
• C. Use Cloud Dataprep to find null values in sample source dat
• D. Convert all nulls to 0 using a Cloud Dataprep job.
• E. Use Cloud Dataflow to find null values in sample source dat
• F. Convert all nulls to ‘none’ using a Cloud Dataprep job.
• G. Use Cloud Dataflow to find null values in sample source dat
• H. Convert all nulls to using a custom script.

Answer: C

NEW QUESTION 14

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

• A. Consume the stream of data in Cloud Dataflow using Kafka I
• B. Set a sliding time window of 1 hour every 5 minute
• C. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
• D. Consume the stream of data in Cloud Dataflow using Kafka I
• E. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
• F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
• G. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
• H. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hou
• I. If that number falls below 4000, send an alert.
• J. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
• K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
• L. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hou
• M. If that number falls below 4000, send an alert.

Answer: C

NEW QUESTION 15

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 without any additional adjustment to your code.
• B. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
• 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: B

NEW QUESTION 16

You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

• A. Assign the users/groups data viewer access at the table level for each table
• B. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
• C. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
• D. Create authorized views for each team in datasets created for each tea
• E. Assign the authorized views data viewer access to the dataset in which the data reside
• F. Assign the users/groups data viewer access to the datasets in which the authorized views reside

Answer: A

NEW QUESTION 17

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

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

Answer: CD

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

NEW QUESTION 18

You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?

• A. Call out to the service via HTTP
• B. Create the pipeline statically in the class definition
• C. Create a new object in the startBundle method of DoFn
• D. Batch the job into ten-second increments

Answer: A

NEW QUESTION 19

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

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

Answer: B

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

NEW QUESTION 20

The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster _____.

• A. application node
• B. conditional node
• C. master node
• D. worker node

Answer: C

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix—for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION 21
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