Up To Date Professional-Data-Engineer Prep For Google Professional Data Engineer Exam Certification

NEW QUESTION 1

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 SQL
• B. Cloud Bigtable
• C. Cloud Spanner
• D. Cloud Datastore

Answer: C

NEW QUESTION 2

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 groups for your users and give those groups access to the 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 accoun
• D. Use the service account’s private key to access the dataset
• E. Create a dummy user and grant dataset access to that use
• F. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

Answer: C

NEW QUESTION 3

Which of these operations can you perform from the BigQuery Web UI?

• A. Upload a file in SQL format.
• B. Load data with nested and repeated fields.
• C. Upload a 20 MB file.
• D. Upload multiple files using a wildcard.

Answer: B

Explanation:
You can load data with nested and repeated fields using the Web UI. You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command. Reference: https://cloud.google.com/bigquery/loading-data

NEW QUESTION 4

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
• B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Answer: D

NEW QUESTION 5

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

• A. Supervised learning to determine which transactions are most likely to be fraudulent.
• B. Unsupervised learning to determine which transactions are most likely to be fraudulent.
• C. Clustering to divide the transactions into N categories based on feature similarity.
• D. Supervised learning to predict the location of a transaction.
• E. Reinforcement learning to predict the location of a transaction.
• F. Unsupervised learning to predict the location of a transaction.

Answer: BCE

NEW QUESTION 6

Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

• A. Add a node to the MySQL cluster and build an OLAP cube there.
• B. Use an ETL tool to load the data from MySQL into Google BigQuery.
• C. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
• D. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

Answer: C

NEW QUESTION 7

What are all of the BigQuery operations that Google charges for?

• A. Storage, queries, and streaming inserts
• B. Storage, queries, and loading data from a file
• C. Storage, queries, and exporting data
• D. Queries and streaming inserts

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION 8

You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

• A. Use Transfer Appliance to copy the data to Cloud Storage
• B. Use gsutil cp –J to compress the content being uploaded to Cloud Storage
• C. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
• D. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic

Answer: A

NEW QUESTION 9

Which of the following statements about Legacy SQL and Standard SQL is not true?

• A. Standard SQL is the preferred query language for BigQuery.
• B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
• C. One difference between the two query languages is how you specify fully-qualified table names (i.
• D. table names that include their associated project name).
• E. You need to set a query language for each dataset and the default is Standard SQL.

Answer: D

Explanation:
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released.
In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql

NEW QUESTION 10

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 11

You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the “Trust No One” (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

• A. Use gcloud kms keys create to create a symmetric ke
• B. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
• C. Use gcloud kms keys create to create a symmetric ke
• D. Then use gcloud kms encrypt to encrypt each archival file with the ke
• E. Use gsutil cp to upload each encrypted file to the Cloud Storage bucke
• F. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.
• G. Specify customer-supplied encryption key (CSEK) in the .boto configuration fil
• H. Use gsutil cp to upload each archival file to the Cloud Storage bucke
• I. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
• J. Specify customer-supplied encryption key (CSEK) in the .boto configuration fil
• K. Use gsutil cp to upload each archival file to the Cloud Storage bucke
• L. Save the CSEK in a different project that only the security team can access.

Answer: B

NEW QUESTION 12

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

• A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
• B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
• C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
• D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Answer: A

NEW QUESTION 13

Which of these sources can you not load data into BigQuery from?

• A. File upload
• B. Google Drive
• C. Google Cloud Storage
• D. Google Cloud SQL

Answer: D

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data

NEW QUESTION 14

Which action can a Cloud Dataproc Viewer perform?

• A. Submit a job.
• B. Create a cluster.
• C. Delete a cluster.
• D. List the jobs.

Answer: D

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 15

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 16

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. Threading
• B. Serialization
• C. Dropout Methods
• D. Dimensionality Reduction

Answer: C

Explanation:
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505

NEW QUESTION 17

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: AB

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 18

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 19

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

• A. Store the common data in BigQuery as partitioned tables.
• B. Store the common data in BigQuery and expose authorized views.
• C. Store the common data encoded as Avro in Google Cloud Storage.
• D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Answer: B

NEW QUESTION 20

What are the minimum permissions needed for a service account used with Google Dataproc?

• A. Execute 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; execute to Google Cloud Logging
• D. Read and write to Google Cloud Storage; write to Google Cloud Logging

Answer: D

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.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION 21

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. node
• C. label
• D. type

Answer: A

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 22

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 a table called tracking_table and include a DATE column.
• B. Create a partitioned table called tracking_table and include a TIMESTAMP column.
• C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
• D. Create a table called tracking_table with a TIMESTAMP column to represent the day.

Answer: B

NEW QUESTION 23

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

• A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
• B. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
• C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
• D. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

Answer: D

NEW QUESTION 24

An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

• A. Create and share an authorized view that provides the aggregate results.
• B. Create and share a new dataset and view that provides the aggregate results.
• C. Create and share a new dataset and table that contains the aggregate results.
• D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Answer: D

Explanation:
Reference: https://cloud.google.com/bigquery/docs/access-control

NEW QUESTION 25
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