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NEW QUESTION: 1
When a Docker container is run from an image on a Docker host, is this how the container is deployed?
If the image is not stored locally on the host, the image is pulled from Docker Hub or the registry indicated in the run command.
A. No
B. Yes
Answer: B
Explanation:
Reference:
https://assets.ext.hpe.com/is/content/hpedam/documents/a00008000-8999/a00008645/ a00008645enw.pdf

NEW QUESTION: 2
Which two actions in a warehousing RAC database may cause concurrent cross-instance calls leading to I/O contention?
A. insert statements where each instance inserts into different partitions of a partitioned table
B. drop table statements
C. select statements referring to non-partitioned tables
D. truncate table statements
Answer: B,D
Explanation:
Concurrent Cross-Instance Calls: Considerations
In data warehouse and data mart environments, it is not uncommon to see a lot of TRUNCATE
operations.
These essentially happen on tables containing temporary data. In a RAC environment, truncating
tables concurrently from different instances does not scale well, especially if, in conjunction, you
are also using direct read operations such as parallel queries.
As shown in the slide, a truncate operation requires a cross-instance call to flush dirty blocks of
the table that may be spread across instances. This constitutes a point of serialization. So, while
the first TRUNCATE command is processing, the second has to wait until the first one completes.
There are different types of cross-instance calls. However, all use the same serialization
mechanism.
For example, the cache flush for a partitioned table with many partitions may add latency to a
corresponding parallel query. This is because each cross-instance call is serialized at the cluster
level, and one crossinstance call is needed for each partition at the start of the parallel query for
direct read purposes.
D60488GC11
Oracle 11g: RAC and Grid Infrastructure Administration Accelerated 14 - 27
What Application Design considerations should I be aware of when moving to Oracle RAC?
The general principals are that fundamentally no different design and coding practices are
required for RAC however application flaws in execution or design have a higher impact in RAC.
The performance and scalability in RAC will be more sensitive to bad plans or bad schema design.
Serializing contention makes applications less scalable. If your customer uses standard SQL and
schema tuning, it solves > 80% of performance problems
Some of the scaleability pitfalls they should look for are:
*Serializing contention on a small set of data/index blocks --> monotonically increasing key --> frequent updates of small cached tables --> segment without automatic segment space management (ASSM) or Free List Group (FLG)
*Full table scans --> Optimization for full scans in 11g can save CPU and latency
*Frequent invalidation and parsing of cursors
--> Requires data dictionary lookups and synchronizations
*Concurrent DDL ( e.g. truncate/drop )
Look for:
*Indexes with right-growing characteristics
--> Use reverse key indexes
--> Eliminate indexes which are not needed
*Frequent updated and reads of "small" tables
--> "small"=fits into a single buffer cache
--> Use Sparse blocks ( PCTFREE 99 ) to reduce serialization
*SQL which scans large amount of data
--> Perhaps more efficient when parallelized
--> Direct reads do not need to be globally synchronized ( hence less CPU for global cache )
RAC: Frequently Asked Questions [ID 220970.1]

NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
* A workload for data engineers who will use Python and SQL
* A workload for jobs that will run notebooks that use Python, Spark, Scala, and SQL
* A workload that data scientists will use to perform ad hoc analysis in Scala and R The enterprise architecture team at your company identifies the following standards for Databricks environments:
* The data engineers must share a cluster.
* The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
* All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databrick clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a High Concurrency cluster for the jobs.
Does this meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Topic 2, Proseware Inc
Background
Proseware, Inc, develops and manages a product named Poll Taker. The product is used for delivering public opinion polling and analysis.
Polling data comes from a variety of sources, including online surveys, house-to-house interviews, and booths at public events.
Polling data
Polling data is stored in one of the two locations:
* An on-premises Microsoft SQL Server 2019 database named PollingData
* Azure Data Lake Gen 2
Data in Data Lake is queried by using PolyBase
Poll metadata
Each poll has associated metadata with information about the poll including the date and number of respondents. The data is stored as JSON.
Phone-based polling
Security
* Phone-based poll data must only be uploaded by authorized users from authorized devices
* Contractors must not have access to any polling data other than their own
* Access to polling data must set on a per-active directory user basis
Data migration and loading
* All data migration processes must use Azure Data Factory
* All data migrations must run automatically during non-business hours
* Data migrations must be reliable and retry when needed
Performance
After six months, raw polling data should be moved to a lower-cost storage solution.
Deployments
* All deployments must be performed by using Azure DevOps. Deployments must use templates used in multiple environments
* No credentials or secrets should be used during deployments
Reliability
All services and processes must be resilient to a regional Azure outage.
Monitoring
All Azure services must be monitored by using Azure Monitor. On-premises SQL Server performance must be monitored.